Data Structures
struct	InterpolationVec
	Class for computing interpolation weights. More...

struct	InterpolationVec< T, VECSIZE, InterpolationMode::NEAREST_NEIGHBOR >
	Implementation for NEAREST_NEIGHBOR. More...

struct	InterpolationVec< T, VECSIZE, InterpolationMode::LINEAR >
	Implementation for LINEAR (uses coordinate clamping) More...

struct	InterpolationVec< T, VECSIZE, InterpolationMode::LINEAR_BORDER >
	Implementation for LINEAR_BORDER (uses zero border instead of clamping) More...

class	MemoryAllocation
	A class for managing memory segments within a memory allocation. More...

class	Adaptor
	Adaptor for nanoflann. More...

struct	SelectNanoflannAdaptor

struct	SelectNanoflannAdaptor< L2, T >

struct	SelectNanoflannAdaptor< L1, T >

class	Accumulator

class	AccumulatorBackprop

Enumerations
enum class	InterpolationMode { LINEAR , LINEAR_BORDER , NEAREST_NEIGHBOR }

enum class	CoordinateMapping { BALL_TO_CUBE_RADIAL , BALL_TO_CUBE_VOLUME_PRESERVING , IDENTITY }

enum	Metric { L1 , L2 , Linf }
	Supported metrics. More...

enum	AccumulationFn { AVERAGE = 0 , NEAREST_NEIGHBOR , NEAREST_NEIGHBOR , MAX , CENTER }

Functions
template<class TFeat , class TOut , class TReal , class TIndex , InterpolationMode INTERPOLATION, CoordinateMapping MAPPING, bool ALIGN_CORNERS, bool INDIVIDUAL_EXTENT, bool ISOTROPIC_EXTENT, bool POINT_IMPORTANCE>
void	_CConvComputeFeaturesCPU (TOut out_features, const std::vector< int > &filter_dims, const TFeat filter, size_t num_out, const TReal out_positions, size_t num_inp, const TReal inp_positions, const TFeat inp_features, const TFeat inp_importance, size_t neighbors_index_size, const TIndex neighbors_index, const TFeat neighbors_importance, const int64_t neighbors_row_splits, const TReal extents, const TReal *offsets, bool normalize)

template<class TFeat , class TOut , class TReal , class TIndex >
void	CConvComputeFeaturesCPU (TOut out_features, const std::vector< int > &filter_dims, const TFeat filter, size_t num_out, const TReal out_positions, size_t num_inp, const TReal inp_positions, const TFeat inp_features, const TFeat inp_importance, size_t neighbors_index_size, const TIndex neighbors_index, const TFeat neighbors_importance, const int64_t neighbors_row_splits, const TReal extents, const TReal *offsets, InterpolationMode interpolation, CoordinateMapping coordinate_mapping, bool align_corners, bool individual_extent, bool isotropic_extent, bool normalize)

template<class TFeat , class TOut , class TReal , class TIndex , InterpolationMode INTERPOLATION, CoordinateMapping MAPPING, bool ALIGN_CORNERS, bool INDIVIDUAL_EXTENT, bool ISOTROPIC_EXTENT, bool POINT_IMPORTANCE>
void	_CConvBackropFilterCPU (TOut filter_backprop, const std::vector< int > &filter_dims, size_t num_out, const TReal out_positions, size_t num_inp, const TReal inp_positions, const TFeat inp_features, const TFeat inp_importance, size_t neighbors_index_size, const TIndex neighbors_index, const TFeat neighbors_importance, const int64_t neighbors_row_splits, const TReal extents, const TReal offsets, const TFeat *out_features_gradient, bool normalize)

template<class TFeat , class TOut , class TReal , class TIndex >
void	CConvBackpropFilterCPU (TOut filter_backprop, const std::vector< int > &filter_dims, size_t num_out, const TReal out_positions, size_t num_inp, const TReal inp_positions, const TFeat inp_features, const TFeat inp_importance, size_t neighbors_index_size, const TIndex neighbors_index, const TFeat neighbors_importance, const int64_t neighbors_row_splits, const TReal extents, const TReal offsets, const TFeat *out_features_gradient, InterpolationMode interpolation, CoordinateMapping coordinate_mapping, bool align_corners, bool individual_extent, bool isotropic_extent, bool normalize)

template<class TFeat , class TReal , class TIndex >
void	FillColumn (const cudaStream_t &stream, TFeat columns, int in_channels, TIndex begin_idx, TIndex end_idx, TIndex num_out, const TReal const __restrict__ out_positions, TIndex num_inp, const TReal const __restrict__ inp_positions, const TFeat const __restrict__ inp_features, const TFeat const __restrict__ inp_importance, size_t neighbors_index_size, const TIndex const __restrict__ neighbors_index, const TFeat const __restrict__ neighbors_importance, const int64_t const __restrict__ neighbors_row_splits, const TReal const __restrict__ extents, const TReal const __restrict__ offsets, const std::vector< int > &filter_dims, InterpolationMode interpolation, CoordinateMapping coordinate_mapping, bool align_corners, bool individual_extent, bool isotropic_extent, bool normalize)

template<class TFeat , class TReal , class TIndex >
void	FillColumnTranspose (const cudaStream_t &stream, TFeat columns, int in_channels, TIndex begin_idx, TIndex end_idx, TIndex num_out, const TReal const __restrict__ out_positions, TIndex num_inp, const TReal const __restrict__ inp_positions, const TFeat const __restrict__ inp_features, const TFeat const __restrict__ inp_neighbors_importance_sum, const int64_t const __restrict__ inp_neighbors_prefix_sum, size_t neighbors_index_size, const TIndex const __restrict__ neighbors_index, const TFeat const __restrict__ neighbors_importance, const int64_t const __restrict__ neighbors_row_splits, const TReal const __restrict__ extents, const TReal *const __restrict__ offsets, const std::vector< int > &filter_dims, InterpolationMode interpolation, CoordinateMapping coordinate_mapping, bool align_corners, bool individual_extent, bool isotropic_extent, bool normalize)

template<class T >
void	MultiplyColumns (const cudaStream_t &stream, size_t rows, size_t cols, T __restrict__ col_major_matrix, const T const __restrict__ vector)

template<class T >
void	MultiplyAndCopyColumns (const cudaStream_t &stream, size_t rows, size_t cols, T __restrict__ out_ptr, const T const __restrict__ col_major_matrix, const T *const __restrict__ vector)

template<class TFeat , class TOut , class TReal , class TIndex , InterpolationMode INTERPOLATION, CoordinateMapping MAPPING, bool ALIGN_CORNERS, bool INDIVIDUAL_EXTENT, bool ISOTROPIC_EXTENT, bool NORMALIZE>
void	_CConvTransposeComputeFeaturesCPU (TOut out_features, const std::vector< int > &filter_dims, const TFeat filter, size_t num_out, const TReal out_positions, const TFeat out_importance, size_t num_inp, const TReal inp_positions, const TFeat inp_features, const TFeat inp_neighbors_importance_sum, const int64_t inp_neighbors_row_splits, size_t neighbors_index_size, const TIndex neighbors_index, const TFeat neighbors_importance, const int64_t neighbors_row_splits, const TReal extents, const TReal *offsets)

template<class TFeat , class TOut , class TReal , class TIndex >
void	CConvTransposeComputeFeaturesCPU (TOut out_features, const std::vector< int > &filter_dims, const TFeat filter, size_t num_out, const TReal out_positions, const TFeat out_importance, size_t num_inp, const TReal inp_positions, const TFeat inp_features, const TFeat inp_neighbors_importance_sum, const int64_t inp_neighbors_row_splits, size_t neighbors_index_size, const TIndex neighbors_index, const TFeat neighbors_importance, const int64_t neighbors_row_splits, const TReal extents, const TReal *offsets, InterpolationMode interpolation, CoordinateMapping coordinate_mapping, bool align_corners, bool individual_extent, bool isotropic_extent, bool normalize)

template<class TFeat , class TOut , class TReal , class TIndex , InterpolationMode INTERPOLATION, CoordinateMapping MAPPING, bool ALIGN_CORNERS, bool INDIVIDUAL_EXTENT, bool ISOTROPIC_EXTENT, bool NORMALIZE>
void	_CConvTransposeBackpropFilterCPU (TOut filter_backprop, const std::vector< int > &filter_dims, size_t num_out, const TReal out_positions, const TFeat out_importance, size_t num_inp, const TReal inp_positions, const TFeat inp_features, const TFeat inp_neighbors_importance_sum, const int64_t inp_neighbors_row_splits, size_t neighbors_index_size, const TIndex neighbors_index, const TFeat neighbors_importance, const int64_t neighbors_row_splits, const TReal extents, const TReal offsets, const TFeat *out_features_gradient)

template<class TFeat , class TOut , class TReal , class TIndex >
void	CConvTransposeBackpropFilterCPU (TOut filter_backprop, const std::vector< int > &filter_dims, size_t num_out, const TReal out_positions, const TFeat out_importance, size_t num_inp, const TReal inp_positions, const TFeat inp_features, const TFeat inp_neighbors_importance_sum, const int64_t inp_neighbors_row_splits, size_t neighbors_index_size, const TIndex neighbors_index, const TFeat neighbors_importance, const int64_t neighbors_row_splits, const TReal extents, const TReal offsets, const TFeat *out_features_gradient, InterpolationMode interpolation, CoordinateMapping coordinate_mapping, bool align_corners, bool individual_extent, bool isotropic_extent, bool normalize)

template<class T , int VECSIZE>
void	MapSphereToCylinder (Eigen::Array< T, VECSIZE, 1 > &x, Eigen::Array< T, VECSIZE, 1 > &y, Eigen::Array< T, VECSIZE, 1 > &z)

template<class T , int VECSIZE>
void	MapCylinderToCube (Eigen::Array< T, VECSIZE, 1 > &x, Eigen::Array< T, VECSIZE, 1 > &y, Eigen::Array< T, VECSIZE, 1 > &z)

template<bool ALIGN_CORNERS, CoordinateMapping MAPPING, class T , int VECSIZE>
void	ComputeFilterCoordinates (Eigen::Array< T, VECSIZE, 1 > &x, Eigen::Array< T, VECSIZE, 1 > &y, Eigen::Array< T, VECSIZE, 1 > &z, const Eigen::Array< int, 3, 1 > &filter_size, const Eigen::Array< T, VECSIZE, 3 > &inv_extents, const Eigen::Array< T, 3, 1 > &offset)

template<class TIndex , class TAttr >
void	InvertNeighborsListCPU (const TIndex const inp_neighbors_index, const TAttr const inp_neighbors_attributes, const int num_attributes_per_neighbor, const int64_t const inp_neighbors_row_splits, const size_t inp_num_queries, TIndex out_neighbors_index, TAttr out_neighbors_attributes, const size_t index_size, int64_t out_neighbors_row_splits, const size_t out_num_queries)

HOST_DEVICE size_t	SpatialHash (int x, int y, int z)
	Spatial hashing function for integer coordinates. More...

HOST_DEVICE size_t	SpatialHash (const utility::MiniVec< int, 3 > &xyz)

template<class TVecf >
HOST_DEVICE utility::MiniVec< int, 3 >	ComputeVoxelIndex (const TVecf &pos, const typename TVecf::Scalar_t &inv_voxel_size)

template<class T >
void	RaggedToDenseCPU (const T const values, const int64_t const row_splits, const size_t row_splits_size, const size_t out_col_size, const T const default_value, const size_t default_value_size, T out_values)

template<class T >
void	ReduceSubarraysSumCPU (const T const values, const size_t values_size, const int64_t const row_splits, const size_t num_arrays, T *out_sums)

template<class T , int NDIM, class OUTPUT_ALLOCATOR >
void	VoxelizeCPU (const size_t num_points, const T const points, const size_t batch_size, const int64_t const row_splits, const T const voxel_size, const T const points_range_min, const T *const points_range_max, const int64_t max_points_per_voxel, const int64_t max_voxels, OUTPUT_ALLOCATOR &output_allocator)

template<class T >
bool	CheckVoxelSize (std::string &err, const size_t num_positions, const T *const positions, const T voxel_size)
	Function for debugging. Checks if the voxel size is too small. More...

template<class TDerived >
Eigen::Vector3i	ComputeVoxelIndex (const Eigen::ArrayBase< TDerived > &pos, const typename TDerived::Scalar &inv_voxel_size)

template<class TReal , class TFeat , class ACCUMULATOR , class OUTPUT_ALLOCATOR >
void	_VoxelPooling (size_t num_inp, const TReal const inp_positions, int in_channels, const TFeat inp_features, TReal voxel_size, OUTPUT_ALLOCATOR &output_allocator)

template<class TReal , class TFeat , class ACCUMULATOR , AccumulationFn FEAT_FN>
void	_VoxelPoolingBackprop (TFeat features_backprop, size_t num_inp, const TReal const inp_positions, int in_channels, const TFeat const inp_features, size_t num_pooled, const TReal const pooled_positions, const TFeat *const pooled_features_gradient, TReal voxel_size)

template<class TReal , class TFeat , class OUTPUT_ALLOCATOR >
void	VoxelPooling (size_t num_inp, const TReal const inp_positions, int in_channels, const TFeat inp_features, TReal voxel_size, OUTPUT_ALLOCATOR &output_allocator, AccumulationFn position_fn, AccumulationFn feature_fn)

template<class TReal , class TFeat >
void	VoxelPoolingBackprop (TFeat features_backprop, size_t num_inp, const TReal const inp_positions, int in_channels, const TFeat const inp_features, size_t num_pooled, const TReal const pooled_positions, const TFeat *const pooled_features_gradient, TReal voxel_size, AccumulationFn position_fn, AccumulationFn feature_fn)

template<class TFeat , class TOut , class TIndex , class TKernelIndex , bool POINT_IMPORTANCE>
void	_SparseConvComputeFeaturesCPU (TOut out_features, const std::vector< int > &filter_dims, const TFeat filter, size_t num_out, size_t num_inp, const TFeat inp_features, const TFeat inp_importance, size_t neighbors_index_size, const TIndex neighbors_index, const TKernelIndex neighbors_kernel_index, const TFeat neighbors_importance, const int64_t neighbors_row_splits, bool normalize)

template<class TFeat , class TOut , class TIndex , class TKernelIndex >
void	SparseConvComputeFeaturesCPU (TOut out_features, const std::vector< int > &filter_dims, const TFeat filter, size_t num_out, size_t num_inp, const TFeat inp_features, const TFeat inp_importance, size_t neighbors_index_size, const TIndex neighbors_index, const TKernelIndex neighbors_kernel_index, const TFeat neighbors_importance, const int64_t neighbors_row_splits, bool normalize)

template<class TFeat , class TOut , class TIndex , class TKernelIndex , bool POINT_IMPORTANCE>
void	_SparseConvBackropFilterCPU (TOut filter_backprop, const std::vector< int > &filter_dims, size_t num_out, size_t num_inp, const TFeat inp_features, const TFeat inp_importance, const TIndex neighbors_index, const TKernelIndex neighbors_kernel_index, const TFeat neighbors_importance, const int64_t neighbors_row_splits, const TFeat out_features_gradient, bool normalize)

template<class TFeat , class TOut , class TIndex , class TKernelIndex >
void	SparseConvBackpropFilterCPU (TOut filter_backprop, const std::vector< int > &filter_dims, size_t num_out, size_t num_inp, const TFeat inp_features, const TFeat inp_importance, const TIndex neighbors_index, const TKernelIndex neighbors_kernel_index, const TFeat neighbors_importance, const int64_t neighbors_row_splits, const TFeat out_features_gradient, bool normalize)

template<class TReal , class TIndex , class TKernelIndex >
void	FillColumn (const cudaStream_t &stream, TReal columns, int in_channels, TIndex begin_idx, TIndex end_idx, TIndex num_out, TIndex num_inp, const TReal const __restrict__ inp_features, const TReal const __restrict__ inp_importance, size_t neighbors_index_size, const TIndex const __restrict__ neighbors_index, const TKernelIndex const __restrict__ neighbors_kernel_index, const TReal const __restrict__ neighbors_importance, const int64_t *const __restrict__ neighbors_row_splits, const int num_kernel_elements, bool normalize)

template<class TReal , class TIndex , class TKernelIndex >
void	FillColumnTranspose (const cudaStream_t &stream, TReal columns, int in_channels, TIndex begin_idx, TIndex end_idx, TIndex num_out, TIndex num_inp, const TReal const __restrict__ inp_features, const TReal const __restrict__ inp_neighbors_importance_sum, const int64_t const __restrict__ inp_neighbors_prefix_sum, size_t neighbors_index_size, const TIndex const __restrict__ neighbors_index, const TKernelIndex const __restrict__ neighbors_kernel_index, const TReal const __restrict__ neighbors_importance, const int64_t const __restrict__ neighbors_row_splits, const int num_kernel_elements, bool normalize)

template<class TFeat , class TOut , class TIndex , class TKernelIndex , bool NORMALIZE>
void	_SparseConvTransposeComputeFeaturesCPU (TOut out_features, const std::vector< int > &filter_dims, const TFeat filter, size_t num_out, const TFeat out_importance, size_t num_inp, const TFeat inp_features, const TFeat inp_neighbors_importance_sum, const int64_t inp_neighbors_row_splits, const TIndex neighbor_index, const TKernelIndex neighbors_kernel_index, const TFeat neighbor_importance, const int64_t neighbors_row_splits)

template<class TFeat , class TOut , class TIndex , class TKernelIndex >
void	SparseConvTransposeComputeFeaturesCPU (TOut out_features, const std::vector< int > &filter_dims, const TFeat filter, size_t num_out, const TFeat out_importance, size_t num_inp, const TFeat inp_features, const TFeat inp_neighbors_importance_sum, const int64_t inp_neighbors_row_splits, const TIndex neighbor_index, const TKernelIndex neighbors_kernel_index, const TFeat neighbor_importance, const int64_t neighbors_row_splits, bool normalize)

template<class TFeat , class TOut , class TIndex , class TKernelIndex , bool NORMALIZE>
void	_SparseConvTransposeBackpropFilterCPU (TOut filter_backprop, const std::vector< int > &filter_dims, size_t num_out, const TFeat out_importance, size_t num_inp, const TFeat inp_features, const TFeat inp_neighbors_importance_sum, const int64_t inp_neighbors_row_splits, const TIndex neighbors_index, const TKernelIndex neighbors_kernel_index, const TFeat neighbors_importance, const int64_t neighbors_row_splits, const TFeat out_features_gradient)

template<class TFeat , class TOut , class TIndex , class TKernelIndex >
void	SparseConvTransposeBackpropFilterCPU (TOut filter_backprop, const std::vector< int > &filter_dims, size_t num_out, const TFeat out_importance, size_t num_inp, const TFeat inp_features, const TFeat inp_neighbors_importance_sum, const int64_t inp_neighbors_row_splits, const TIndex neighbors_index, const TKernelIndex neighbors_kernel_index, const TFeat neighbors_importance, const int64_t neighbors_row_splits, const TFeat out_features_gradient, bool normalize)

Enumeration Type Documentation

◆ AccumulationFn

enum open3d::ml::impl::AccumulationFn

Enumerator
AVERAGE
NEAREST_NEIGHBOR
NEAREST_NEIGHBOR
MAX
CENTER

◆ CoordinateMapping

enum open3d::ml::impl::CoordinateMapping

strong

Coordinate Mapping functions

BALL_TO_CUBE_RADIAL uses radial stretching to map a sphere to a cube.
BALL_TO_CUBE_VOLUME_PRESERVING is using a more expensive volume preserving mapping to map a sphere to a cube.
IDENTITY no mapping is applied to the coordinates.

Enumerator
BALL_TO_CUBE_RADIAL
BALL_TO_CUBE_VOLUME_PRESERVING
IDENTITY

◆ InterpolationMode

enum open3d::ml::impl::InterpolationMode

strong

Interpolation modes LINEAR is a standard trilinear interpolation with coordinate clamping LINEAR_BORDER uses a zero border instead of clamping NEAREST_NEIGHBOR no interpolation, use nearest neighbor

Enumerator
LINEAR
LINEAR_BORDER
NEAREST_NEIGHBOR

◆ Metric

enum open3d::ml::impl::Metric

Supported metrics.

Enumerator
L1
L2
Linf

Function Documentation

◆ _CConvBackropFilterCPU()

template<class TFeat , class TOut , class TReal , class TIndex , InterpolationMode INTERPOLATION, CoordinateMapping MAPPING, bool ALIGN_CORNERS, bool INDIVIDUAL_EXTENT, bool ISOTROPIC_EXTENT, bool POINT_IMPORTANCE>

void open3d::ml::impl::_CConvBackropFilterCPU	(	TOut *	filter_backprop,
		const std::vector< int > &	filter_dims,
		size_t	num_out,
		const TReal *	out_positions,
		size_t	num_inp,
		const TReal *	inp_positions,
		const TFeat *	inp_features,
		const TFeat *	inp_importance,
		size_t	neighbors_index_size,
		const TIndex *	neighbors_index,
		const TFeat *	neighbors_importance,
		const int64_t *	neighbors_row_splits,
		const TReal *	extents,
		const TReal *	offsets,
		const TFeat *	out_features_gradient,
		bool	normalize
	)

◆ _CConvComputeFeaturesCPU()

template<class TFeat , class TOut , class TReal , class TIndex , InterpolationMode INTERPOLATION, CoordinateMapping MAPPING, bool ALIGN_CORNERS, bool INDIVIDUAL_EXTENT, bool ISOTROPIC_EXTENT, bool POINT_IMPORTANCE>

void open3d::ml::impl::_CConvComputeFeaturesCPU	(	TOut *	out_features,
		const std::vector< int > &	filter_dims,
		const TFeat *	filter,
		size_t	num_out,
		const TReal *	out_positions,
		size_t	num_inp,
		const TReal *	inp_positions,
		const TFeat *	inp_features,
		const TFeat *	inp_importance,
		size_t	neighbors_index_size,
		const TIndex *	neighbors_index,
		const TFeat *	neighbors_importance,
		const int64_t *	neighbors_row_splits,
		const TReal *	extents,
		const TReal *	offsets,
		bool	normalize
	)

Implementation of CConvComputeFeatures with template parameters for configuration.

◆ _CConvTransposeBackpropFilterCPU()

template<class TFeat , class TOut , class TReal , class TIndex , InterpolationMode INTERPOLATION, CoordinateMapping MAPPING, bool ALIGN_CORNERS, bool INDIVIDUAL_EXTENT, bool ISOTROPIC_EXTENT, bool NORMALIZE>

void open3d::ml::impl::_CConvTransposeBackpropFilterCPU	(	TOut *	filter_backprop,
		const std::vector< int > &	filter_dims,
		size_t	num_out,
		const TReal *	out_positions,
		const TFeat *	out_importance,
		size_t	num_inp,
		const TReal *	inp_positions,
		const TFeat *	inp_features,
		const TFeat *	inp_neighbors_importance_sum,
		const int64_t *	inp_neighbors_row_splits,
		size_t	neighbors_index_size,
		const TIndex *	neighbors_index,
		const TFeat *	neighbors_importance,
		const int64_t *	neighbors_row_splits,
		const TReal *	extents,
		const TReal *	offsets,
		const TFeat *	out_features_gradient
	)

Implementation of CConvTransposeBackpropFilterCPU with template parameters for configuration.

◆ _CConvTransposeComputeFeaturesCPU()

template<class TFeat , class TOut , class TReal , class TIndex , InterpolationMode INTERPOLATION, CoordinateMapping MAPPING, bool ALIGN_CORNERS, bool INDIVIDUAL_EXTENT, bool ISOTROPIC_EXTENT, bool NORMALIZE>

void open3d::ml::impl::_CConvTransposeComputeFeaturesCPU	(	TOut *	out_features,
		const std::vector< int > &	filter_dims,
		const TFeat *	filter,
		size_t	num_out,
		const TReal *	out_positions,
		const TFeat *	out_importance,
		size_t	num_inp,
		const TReal *	inp_positions,
		const TFeat *	inp_features,
		const TFeat *	inp_neighbors_importance_sum,
		const int64_t *	inp_neighbors_row_splits,
		size_t	neighbors_index_size,
		const TIndex *	neighbors_index,
		const TFeat *	neighbors_importance,
		const int64_t *	neighbors_row_splits,
		const TReal *	extents,
		const TReal *	offsets
	)

Implementation of CConvComputeFeatures with template parameters for configuration.

◆ _SparseConvBackropFilterCPU()

template<class TFeat , class TOut , class TIndex , class TKernelIndex , bool POINT_IMPORTANCE>

void open3d::ml::impl::_SparseConvBackropFilterCPU	(	TOut *	filter_backprop,
		const std::vector< int > &	filter_dims,
		size_t	num_out,
		size_t	num_inp,
		const TFeat *	inp_features,
		const TFeat *	inp_importance,
		const TIndex *	neighbors_index,
		const TKernelIndex *	neighbors_kernel_index,
		const TFeat *	neighbors_importance,
		const int64_t *	neighbors_row_splits,
		const TFeat *	out_features_gradient,
		bool	normalize
	)

◆ _SparseConvComputeFeaturesCPU()

template<class TFeat , class TOut , class TIndex , class TKernelIndex , bool POINT_IMPORTANCE>

void open3d::ml::impl::_SparseConvComputeFeaturesCPU	(	TOut *	out_features,
		const std::vector< int > &	filter_dims,
		const TFeat *	filter,
		size_t	num_out,
		size_t	num_inp,
		const TFeat *	inp_features,
		const TFeat *	inp_importance,
		size_t	neighbors_index_size,
		const TIndex *	neighbors_index,
		const TKernelIndex *	neighbors_kernel_index,
		const TFeat *	neighbors_importance,
		const int64_t *	neighbors_row_splits,
		bool	normalize
	)

Implementation of SparseConvComputeFeatures with template parameters for configuration.

◆ _SparseConvTransposeBackpropFilterCPU()

template<class TFeat , class TOut , class TIndex , class TKernelIndex , bool NORMALIZE>

void open3d::ml::impl::_SparseConvTransposeBackpropFilterCPU	(	TOut *	filter_backprop,
		const std::vector< int > &	filter_dims,
		size_t	num_out,
		const TFeat *	out_importance,
		size_t	num_inp,
		const TFeat *	inp_features,
		const TFeat *	inp_neighbors_importance_sum,
		const int64_t *	inp_neighbors_row_splits,
		const TIndex *	neighbors_index,
		const TKernelIndex *	neighbors_kernel_index,
		const TFeat *	neighbors_importance,
		const int64_t *	neighbors_row_splits,
		const TFeat *	out_features_gradient
	)

Implementation of SparseConvTransposeBackpropFilterCPU with template parameters for configuration.

◆ _SparseConvTransposeComputeFeaturesCPU()

template<class TFeat , class TOut , class TIndex , class TKernelIndex , bool NORMALIZE>

void open3d::ml::impl::_SparseConvTransposeComputeFeaturesCPU	(	TOut *	out_features,
		const std::vector< int > &	filter_dims,
		const TFeat *	filter,
		size_t	num_out,
		const TFeat *	out_importance,
		size_t	num_inp,
		const TFeat *	inp_features,
		const TFeat *	inp_neighbors_importance_sum,
		const int64_t *	inp_neighbors_row_splits,
		const TIndex *	neighbor_index,
		const TKernelIndex *	neighbors_kernel_index,
		const TFeat *	neighbor_importance,
		const int64_t *	neighbors_row_splits
	)

Implementation of SparseConvTransposeComputeFeatures with template parameters for configuration.

◆ _VoxelPooling()

template<class TReal , class TFeat , class ACCUMULATOR , class OUTPUT_ALLOCATOR >

void open3d::ml::impl::_VoxelPooling	(	size_t	num_inp,
		const TReal *const	inp_positions,
		int	in_channels,
		const TFeat *	inp_features,
		TReal	voxel_size,
		OUTPUT_ALLOCATOR &	output_allocator
	)

◆ _VoxelPoolingBackprop()

template<class TReal , class TFeat , class ACCUMULATOR , AccumulationFn FEAT_FN>

void open3d::ml::impl::_VoxelPoolingBackprop	(	TFeat *	features_backprop,
		size_t	num_inp,
		const TReal *const	inp_positions,
		int	in_channels,
		const TFeat *const	inp_features,
		size_t	num_pooled,
		const TReal *const	pooled_positions,
		const TFeat *const	pooled_features_gradient,
		TReal	voxel_size
	)

◆ CConvBackpropFilterCPU()

template<class TFeat , class TOut , class TReal , class TIndex >

void open3d::ml::impl::CConvBackpropFilterCPU	(	TOut *	filter_backprop,
		const std::vector< int > &	filter_dims,
		size_t	num_out,
		const TReal *	out_positions,
		size_t	num_inp,
		const TReal *	inp_positions,
		const TFeat *	inp_features,
		const TFeat *	inp_importance,
		size_t	neighbors_index_size,
		const TIndex *	neighbors_index,
		const TFeat *	neighbors_importance,
		const int64_t *	neighbors_row_splits,
		const TReal *	extents,
		const TReal *	offsets,
		const TFeat *	out_features_gradient,
		InterpolationMode	interpolation,
		CoordinateMapping	coordinate_mapping,
		bool	align_corners,
		bool	individual_extent,
		bool	isotropic_extent,
		bool	normalize
	)

Computes the backprop for the filter of a continuous convolution.

Template Parameters

TFeat	Type for the features and weights
TOut	Type for the output features
TReal	Type for point positions and extents
TIndex	Type for neighbor indexing

Parameters

filter_backprop	Output array for the computed filter gradient with shape [depth,height,width, inp channels, out channels]
filter_dims	The sizes of the filter dimensions. The size of filter_dims must be 5. The order is [depth, height, width, inp channels, out channels].
num_out	The number of output points.
out_positions	The positions of the output points. The shape is [num_out, 3].
num_inp	The number of input points.
inp_positions	The positions of the input points. The shape is [num_inp, 3].
inp_features	The input features with shape [num_inp, in_channels].
inp_importance	Optional importance for each input point with shape [num_inp]. Set to null to disable.
neighbors_index_size	The size of the neighbors_index array.
neighbors_index	The array with lists of neighbors for each output point. The start and end of each sublist is defined by `neighbors_row_splits`.
neighbors_importance	Optional importance for each entry in `neighbors_index`. Set to null to disable.
neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `neighbors_index`. The size of the array is `num_out` + 1.
extents	The spatial extents of the filter in coordinate units. extents can be a scalar or a 1D array of shape [num_out] or a 2D array of shape [num_out,3]. The shape depends on `individual_extent` and `isotropic_extent`.
offsets	A single 3D vector used in the filter coordinate computation. The shape is [3].
interpolation	The interpolation mode. Either LINEAR or NEAREST_NEIGHBOR.
coordinate_mapping	The coordinate mapping function. One of IDENTITY, BALL_TO_CUBE_RADIAL, BALL_TO_CUBE_VOLUME_PRESERVING.
align_corners	If true then the voxel centers of the outer voxels of the filter array are mapped to the boundary of the filter shape. If false then the boundary of the filter array is mapped to the boundary of the filter shape.
individual_extent	If true each output point has an individual extent.
isotropic_extent	If true each then the extent is isotropic for each output point.
normalize	If true then the output features are normalized either by the number of points (neighbors_importance is null) or by the sum of the respective values in neighbors_importance.

◆ CConvComputeFeaturesCPU()

template<class TFeat , class TOut , class TReal , class TIndex >

void open3d::ml::impl::CConvComputeFeaturesCPU	(	TOut *	out_features,
		const std::vector< int > &	filter_dims,
		const TFeat *	filter,
		size_t	num_out,
		const TReal *	out_positions,
		size_t	num_inp,
		const TReal *	inp_positions,
		const TFeat *	inp_features,
		const TFeat *	inp_importance,
		size_t	neighbors_index_size,
		const TIndex *	neighbors_index,
		const TFeat *	neighbors_importance,
		const int64_t *	neighbors_row_splits,
		const TReal *	extents,
		const TReal *	offsets,
		InterpolationMode	interpolation,
		CoordinateMapping	coordinate_mapping,
		bool	align_corners,
		bool	individual_extent,
		bool	isotropic_extent,
		bool	normalize
	)

Computes the output features of a continuous convolution.

Template Parameters

TFeat	Type for the features and weights
TOut	Type for the output features
TReal	Type for point positions and extents
TIndex	Type for neighbor indexing

Parameters

out_features	Output array for the computed features with shape [num_out, out channels]
filter_dims	The sizes of the filter dimensions. The size of filter_dims must be 5. The order is [depth, height, width, inp channels, out channels].
filter	Pointer to the filter values.
num_out	The number of output points.
out_positions	The positions of the output points. The shape is [num_out, 3].
num_inp	The number of input points.
inp_positions	The positions of the input points. The shape is [num_inp, 3].
inp_features	The input features with shape [num_inp, in_channels].
inp_importance	Optional importance for each input point with shape [num_inp]. Set to null to disable.
neighbors_index_size	The size of the neighbors_index array.
neighbors_index	The array with lists of neighbors for each output point. The start and end of each sublist is defined by `neighbors_row_splits`.
neighbors_importance	Optional importance for each entry in `neighbors_index`. Set to null to disable.
neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `neighbors_index`. The size of the array is `num_out` + 1.
extents	The spatial extents of the filter in coordinate units. extents can be a scalar or a 1D array of shape [num_out] or a 2D array of shape [num_out,3]. The shape depends on `individual_extent` and `isotropic_extent`.
offsets	A single 3D vector used in the filter coordinate computation. The shape is [3].
interpolation	The interpolation mode. Either LINEAR or NEAREST_NEIGHBOR.
coordinate_mapping	The coordinate mapping function. One of IDENTITY, BALL_TO_CUBE_RADIAL, BALL_TO_CUBE_VOLUME_PRESERVING.
align_corners	If true then the voxel centers of the outer voxels of the filter array are mapped to the boundary of the filter shape. If false then the boundary of the filter array is mapped to the boundary of the filter shape.
individual_extent	If true each output point has an individual extent.
isotropic_extent	If true each then the extent is isotropic for each output point.
normalize	If true then the result is normalized either by the number of points (neighbors_importance is null) or by the sum of the respective values in neighbors_importance.

◆ CConvTransposeBackpropFilterCPU()

template<class TFeat , class TOut , class TReal , class TIndex >

void open3d::ml::impl::CConvTransposeBackpropFilterCPU	(	TOut *	filter_backprop,
		const std::vector< int > &	filter_dims,
		size_t	num_out,
		const TReal *	out_positions,
		const TFeat *	out_importance,
		size_t	num_inp,
		const TReal *	inp_positions,
		const TFeat *	inp_features,
		const TFeat *	inp_neighbors_importance_sum,
		const int64_t *	inp_neighbors_row_splits,
		size_t	neighbors_index_size,
		const TIndex *	neighbors_index,
		const TFeat *	neighbors_importance,
		const int64_t *	neighbors_row_splits,
		const TReal *	extents,
		const TReal *	offsets,
		const TFeat *	out_features_gradient,
		InterpolationMode	interpolation,
		CoordinateMapping	coordinate_mapping,
		bool	align_corners,
		bool	individual_extent,
		bool	isotropic_extent,
		bool	normalize
	)

Computes the backprop for the filter of a transpose continuous convolution.

Template Parameters

TFeat	Type for the features and weights
TOut	Type for the output features
TReal	Type for point positions and extents
TIndex	Type for neighbor indexing

Parameters

filter_backprop	Output array for the computed filter gradient with shape [depth,height,width, inp channels, out channels]
filter_dims	The sizes of the filter dimensions. The size of filter_dims must be 5. The order is [depth, height, width, inp channels, out channels].
num_out	The number of output points.
out_positions	The positions of the output points. The shape is [num_out, 3].
out_importance	Optional importance for each output point with shape [num_out]. Set to null to disable.
num_inp	The number of input points.
inp_positions	The positions of the input points. The shape is [num_inp, 3].
inp_features	The input features with shape [num_inp, in_channels].
inp_neighbors_importance_sum	The sum of the neighbors_importance values for each input with shape [num_inp].
inp_neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `inp_neighbors_index`. The size of the array is `num_inp` + 1.
neighbors_index_size	The size of the neighbors_index array.
neighbors_index	The array with lists of neighbors for each output point. The start and end of each sublist is defined by `neighbors_row_splits`.
neighbors_importance	Optional importance for each entry in `neighbors_index`. Set to null to disable.
neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `neighbors_index`. The size of the array is `num_out` + 1.
extents	The spatial extents of the filter in coordinate units. extents can be a scalar or a 1D array of shape [num_out] or a 2D array of shape [num_out,3]. The shape depends on `individual_extent` and `isotropic_extent`.
offsets	A single 3D vector used in the filter coordinate computation. The shape is [3].
out_features_gradient	The gradient from the features with shape [num_out, out_channels]
interpolation	The interpolation mode. Either LINEAR or NEAREST_NEIGHBOR.
coordinate_mapping	The coordinate mapping function. One of IDENTITY, BALL_TO_CUBE_RADIAL, BALL_TO_CUBE_VOLUME_PRESERVING.
align_corners	If true then the voxel centers of the outer voxels of the filter array are mapped to the boundary of the filter shape. If false then the boundary of the filter array is mapped to the boundary of the filter shape.
individual_extent	If true each output point has an individual extent.
isotropic_extent	If true each then the extent is isotropic for each output point.
normalize	If true then the result is normalized either by the number of points (neighbors_importance is null) or by the sum of the respective values in neighbors_importance.

◆ CConvTransposeComputeFeaturesCPU()

template<class TFeat , class TOut , class TReal , class TIndex >

void open3d::ml::impl::CConvTransposeComputeFeaturesCPU	(	TOut *	out_features,
		const std::vector< int > &	filter_dims,
		const TFeat *	filter,
		size_t	num_out,
		const TReal *	out_positions,
		const TFeat *	out_importance,
		size_t	num_inp,
		const TReal *	inp_positions,
		const TFeat *	inp_features,
		const TFeat *	inp_neighbors_importance_sum,
		const int64_t *	inp_neighbors_row_splits,
		size_t	neighbors_index_size,
		const TIndex *	neighbors_index,
		const TFeat *	neighbors_importance,
		const int64_t *	neighbors_row_splits,
		const TReal *	extents,
		const TReal *	offsets,
		InterpolationMode	interpolation,
		CoordinateMapping	coordinate_mapping,
		bool	align_corners,
		bool	individual_extent,
		bool	isotropic_extent,
		bool	normalize
	)

Computes the output features of a transpose continuous convolution.

Template Parameters

TFeat	Type for the features and weights
TOut	Type for the output features
TReal	Type for point positions and extents
TIndex	Type for neighbor indexing

Parameters

out_features	Output array for the computed features with shape [num_out, out channels]
filter_dims	The sizes of the filter dimensions. The size of filter_dims must be 5. The order is [depth, height, width, inp channels, out channels].
filter	Pointer to the filter values.
num_out	The number of output points.
out_positions	The positions of the output points. The shape is [num_out, 3].
out_importance	Optional importance for each output point with shape [num_out]. Set to null to disable.
num_inp	The number of input points.
inp_positions	The positions of the input points. The shape is [num_inp, 3].
inp_features	The input features with shape [num_inp, in_channels].
inp_neighbors_importance_sum	The sum of the neighbors_importance values for each input with shape [num_inp].
inp_neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `inp_neighbors_index`. The size of the array is `num_inp` + 1.
neighbors_index_size	The size of the neighbors_index array.
neighbors_index	The array with lists of neighbors for each output point. The start and end of each sublist is defined by `neighbors_row_splits`.
neighbors_importance	Optional importance for each entry in `neighbors_index`. Set to null to disable.
neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `neighbors_index`. The size of the array is `num_out` + 1.
extents	The spatial extents of the filter in coordinate units. extents can be a scalar or a 1D array of shape [num_out] or a 2D array of shape [num_out,3]. The shape depends on `individual_extent` and `isotropic_extent`.
offsets	A single 3D vector used in the filter coordinate computation. The shape is [3].
interpolation	The interpolation mode. Either LINEAR or NEAREST_NEIGHBOR.
coordinate_mapping	The coordinate mapping function. One of IDENTITY, BALL_TO_CUBE_RADIAL, BALL_TO_CUBE_VOLUME_PRESERVING.
align_corners	If true then the voxel centers of the outer voxels of the filter array are mapped to the boundary of the filter shape. If false then the boundary of the filter array is mapped to the boundary of the filter shape.
individual_extent	If true each output point has an individual extent.
isotropic_extent	If true each then the extent is isotropic for each output point.
normalize	If true then the result is normalized either by the number of points (neighbors_importance is null) or by the sum of the respective values in neighbors_importance.

◆ CheckVoxelSize()

template<class T >

bool open3d::ml::impl::CheckVoxelSize	(	std::string &	err,
		const size_t	num_positions,
		const T *const	positions,
		const T	voxel_size
	)

Function for debugging. Checks if the voxel size is too small.

◆ ComputeFilterCoordinates()

template<bool ALIGN_CORNERS, CoordinateMapping MAPPING, class T , int VECSIZE>

void open3d::ml::impl::ComputeFilterCoordinates	(	Eigen::Array< T, VECSIZE, 1 > &	x,
		Eigen::Array< T, VECSIZE, 1 > &	y,
		Eigen::Array< T, VECSIZE, 1 > &	z,
		const Eigen::Array< int, 3, 1 > &	filter_size,
		const Eigen::Array< T, VECSIZE, 3 > &	inv_extents,
		const Eigen::Array< T, 3, 1 > &	offset
	)

inline

Computes the filter coordinates. The input to this function are coordinates relative to the point where the convolution is evaluated. Coordinates are usually in the range [-extent/2,extent/2] with extent as the edge length of the bounding box of the filter shape. The output is a coordinate within the filter array, i.e. the range is [0, filter_size.xyz], if the point was inside the filter shape.

The simplest filter shape is a cuboid (MAPPING=IDENTITY) and the transformation is simply [-extent/2,extent/2] -> [0, filter_size.xyz]. The other type of shape that is implemented is a sphere with MAPPING=BALL_TO_CUBE_RADIAL or MAPPING=BALL_TO_CUBE_VOLUME_PRESERVING.

Template Parameters

ALIGN_CORNERS	If true then the voxel centers of the outer voxels of the filter array are mapped to the boundary of the filter shape. If false then the boundary of the filter array is mapped to the boundary of the filter shape.
MAPPING	The mapping that is applied to the input coordinates. BALL_TO_CUBE_RADIAL uses radial stretching to map a sphere to a cube. BALL_TO_CUBE_VOLUME_PRESERVING is using a more expensive volume preserving mapping to map a sphere to a cube. IDENTITY no mapping is applied to the coordinates.

Parameters

x	x coordinates. Input and output variable.
y	y coordinates. Input and output variable.
z	z coordinates. Input and output variable.
filter_size	The spatial size of the filter array in voxels.
inv_extents	The reciproval of the spatial extent of the filter in coordinate units.
offset	An offset for shifting the center. Can be used to implement discrete filters with even filter size.

◆ ComputeVoxelIndex() [1/2]

template<class TDerived >

Eigen::Vector3i open3d::ml::impl::ComputeVoxelIndex	(	const Eigen::ArrayBase< TDerived > &	pos,
		const typename TDerived::Scalar &	inv_voxel_size
	)

Computes an integer voxel index for a 3D position.

Parameters

pos	A 3D position.
inv_voxel_size	The reciprocal of the voxel size

◆ ComputeVoxelIndex() [2/2]

template<class TVecf >

HOST_DEVICE utility::MiniVec<int, 3> open3d::ml::impl::ComputeVoxelIndex	(	const TVecf &	pos,
		const typename TVecf::Scalar_t &	inv_voxel_size
	)

inline

Computes an integer voxel index for a 3D position.

Parameters

pos	A 3D position.
inv_voxel_size	The reciprocal of the voxel size

◆ FillColumn() [1/2]

template<class TFeat , class TReal , class TIndex >

void open3d::ml::impl::FillColumn	(	const cudaStream_t &	stream,
		TFeat *	columns,
		int	in_channels,
		TIndex	begin_idx,
		TIndex	end_idx,
		TIndex	num_out,
		const TReal *const __restrict__	out_positions,
		TIndex	num_inp,
		const TReal *const __restrict__	inp_positions,
		const TFeat *const __restrict__	inp_features,
		const TFeat *const __restrict__	inp_importance,
		size_t	neighbors_index_size,
		const TIndex *const __restrict__	neighbors_index,
		const TFeat *const __restrict__	neighbors_importance,
		const int64_t *const __restrict__	neighbors_row_splits,
		const TReal *const __restrict__	extents,
		const TReal *const __restrict__	offsets,
		const std::vector< int > &	filter_dims,
		InterpolationMode	interpolation,
		CoordinateMapping	coordinate_mapping,
		bool	align_corners,
		bool	individual_extent,
		bool	isotropic_extent,
		bool	normalize
	)

Copies and transforms the features to a column, which can be multiplied with the filter matrix.

Template Parameters

TFeat	Type for the features
TOut	Type for the output features
TReal	Type for point positions and extents
TIndex	Type for neighbor indexing

Parameters

columns	Output array with shape [num_out, spatial filter dims, in_channels].
in_channels	Number of input channels.
begin_idx	Index of the first output point to process.
end_idx	Index after the last output point to process.
num_out	The number of output points.
out_positions	The positions of the output points. The shape is [num_out, 3].
num_inp	The number of input points.
inp_positions	The positions of the input points. The shape is [num_inp, 3].
inp_features	The input features with shape [num_inp, in_channels].
inp_importance	Optional importance for each input point with shape [num_inp]. Set to null to disable.
neighbors_index_size	The size of the neighbors_index array.
neighbors_index	The array with lists of neighbors for each output point. The start and end of each sublist is defined by `neighbors_row_splits`.
neighbors_importance	Optional importance for each entry in `neighbors_index`. Set to null to disable.
neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `neighbors_index`. The size of the array is `num_out` + 1.
extents	The spatial extents of the filter in coordinate units. extents can be a scalar or a 1D array of shape [num_out] or a 2D array of shape [num_out,3]. The shape depends on `individual_extent` and `isotropic_extent`.
offsets	A single 3D vector used in the filter coordinate computation. The shape is [3].
filter_dims	The spatial filter size in voxels. (The filter resolution) with shape [3].
interpolation	The interpolation mode. Either LINEAR or NEAREST_NEIGHBOR.
coordinate_mapping	The coordinate mapping function. One of IDENTITY, BALL_TO_CUBE_RADIAL, BALL_TO_CUBE_VOLUME_PRESERVING.
align_corners	If true then the voxel centers of the outer voxels of the filter array are mapped to the boundary of the filter shape. If false then the boundary of the filter array is mapped to the boundary of the filter shape.
individual_extent	If true each output point has an individual extent.
isotropic_extent	If true each then the extent is isotropic for each output point.
normalize	If true then the result is normalized either by the number of points (neighbors_importance is null) or by the sum of the respective values in neighbors_importance.

◆ FillColumn() [2/2]

template<class TReal , class TIndex , class TKernelIndex >

void open3d::ml::impl::FillColumn	(	const cudaStream_t &	stream,
		TReal *	columns,
		int	in_channels,
		TIndex	begin_idx,
		TIndex	end_idx,
		TIndex	num_out,
		TIndex	num_inp,
		const TReal *const __restrict__	inp_features,
		const TReal *const __restrict__	inp_importance,
		size_t	neighbors_index_size,
		const TIndex *const __restrict__	neighbors_index,
		const TKernelIndex *const __restrict__	neighbors_kernel_index,
		const TReal *const __restrict__	neighbors_importance,
		const int64_t *const __restrict__	neighbors_row_splits,
		const int	num_kernel_elements,
		bool	normalize
	)

Copies and transforms the features to a column, which can be multiplied with the filter matrix.

Template Parameters

TReal	Type for positions and features.
TIndex	Type for addressing neighbors.
TKernelIndex	Type for addressing the spatial dimension in a kernel.

Parameters

columns	Output array with shape [num_out, spatial filter dims, in_channels].
in_channels	Number of input channels.
begin_idx	Index of the first output point to process.
end_idx	Index after the last output point to process.
num_out	The number of output points.
num_inp	The number of input points.
inp_features	The input features with shape [num_inp, in_channels].
inp_importance	Optional importance for each input point with shape [num_inp]. Set to null to disable.
neighbors_index_size	The size of the neighbors_index array.
neighbors_index	The array with lists of neighbors for each output point. The start and end of each sublist is defined by `neighbors_row_splits`.
neighbors_kernel_index	Defines which kernel element to use for each neighbor. This array has the same length as `neighbors_index`.
neighbors_importance	Optional importance for each entry in `neighbors_index`. Set to null to disable.
neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `neighbors_index`. The size of the array is `num_out` + 1.
num_kernel_elements	The number of kernel elements. This is the size of the first dimension of the filter, i.e., The filter shape is [num_kernel_elements, in_channels, out_channels]
normalize	If true then the result is normalized either by the number of points (neighbors_importance is null) or by the sum of the respective values in neighbors_importance.

◆ FillColumnTranspose() [1/2]

template<class TFeat , class TReal , class TIndex >

void open3d::ml::impl::FillColumnTranspose	(	const cudaStream_t &	stream,
		TFeat *	columns,
		int	in_channels,
		TIndex	begin_idx,
		TIndex	end_idx,
		TIndex	num_out,
		const TReal *const __restrict__	out_positions,
		TIndex	num_inp,
		const TReal *const __restrict__	inp_positions,
		const TFeat *const __restrict__	inp_features,
		const TFeat *const __restrict__	inp_neighbors_importance_sum,
		const int64_t *const __restrict__	inp_neighbors_prefix_sum,
		size_t	neighbors_index_size,
		const TIndex *const __restrict__	neighbors_index,
		const TFeat *const __restrict__	neighbors_importance,
		const int64_t *const __restrict__	neighbors_row_splits,
		const TReal *const __restrict__	extents,
		const TReal *const __restrict__	offsets,
		const std::vector< int > &	filter_dims,
		InterpolationMode	interpolation,
		CoordinateMapping	coordinate_mapping,
		bool	align_corners,
		bool	individual_extent,
		bool	isotropic_extent,
		bool	normalize
	)

◆ FillColumnTranspose() [2/2]

template<class TReal , class TIndex , class TKernelIndex >

void open3d::ml::impl::FillColumnTranspose	(	const cudaStream_t &	stream,
		TReal *	columns,
		int	in_channels,
		TIndex	begin_idx,
		TIndex	end_idx,
		TIndex	num_out,
		TIndex	num_inp,
		const TReal *const __restrict__	inp_features,
		const TReal *const __restrict__	inp_neighbors_importance_sum,
		const int64_t *const __restrict__	inp_neighbors_prefix_sum,
		size_t	neighbors_index_size,
		const TIndex *const __restrict__	neighbors_index,
		const TKernelIndex *const __restrict__	neighbors_kernel_index,
		const TReal *const __restrict__	neighbors_importance,
		const int64_t *const __restrict__	neighbors_row_splits,
		const int	num_kernel_elements,
		bool	normalize
	)

Similar as FillColumn but used in the transpose convolution to create the patch matrix.

◆ InvertNeighborsListCPU()

template<class TIndex , class TAttr >

void open3d::ml::impl::InvertNeighborsListCPU	(	const TIndex *const	inp_neighbors_index,
		const TAttr *const	inp_neighbors_attributes,
		const int	num_attributes_per_neighbor,
		const int64_t *const	inp_neighbors_row_splits,
		const size_t	inp_num_queries,
		TIndex *	out_neighbors_index,
		TAttr *	out_neighbors_attributes,
		const size_t	index_size,
		int64_t *	out_neighbors_row_splits,
		const size_t	out_num_queries
	)

Inverts a neighbors list, which is a tuple of the form (neighbors_index, neighbors_row_splits, neighbors_attributes). neighbors_index is a nested list of indices to the neighbors. Each entry defines an edge between two indices (points). The neighbors_row_splits defines the start and end of each sublist. neighbors_attributes is an optional array of attributes for each entry in neighbors_index.

Example: The neighbors for point cloud A (3 points) in point cloud B (2 points) is defined by:

neighbors_index [0 1 0 0]
neighbors_row_splits [0 2 3 4]
optional neighbors_attributes [0.1 0.2 0.3 0.4] (1 scalar attribute)

The inverted neighbors list is then the neighbors for point cloud B in A

neighbors_index [0 1 2 0]
neighbors_row_splits [0 3 4]
optional neighbors_attributes [0.1 0.3 0.4 0.2]

Parameters

inp_neighbors_index	The nested list of neighbor indices.
inp_neighbors_attributes	The array of attributes for each entry in `inp_neighbors_index`. This is optional and can be set to null.
num_attributes_per_neighbor	The number of scalar attributes for each entry in `inp_neighbors_index`.
inp_neighbors_row_splits	Defines the start and end of the sublists in `inp_neighbors_index`. This is an exclusive prefix sum with 0 as the first element and the length of `inp_neighbors_index` as last element. The size is `inp_num_queries` + 1
inp_num_queries	The number of queries.
out_neighbors_index	The inverted neighbors_index list with the same size as `inp_neighbors_index` .
out_neighbors_attributes	The inverted array of attributes with the same size as `inp_neighbors_attributes` .
index_size	This is the size of `inp_neighbors_index` and `out_neighbors_index`, both have the same size.
out_neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `out_neighbors_index`.
out_num_queries	The number of queries with respect to the inverted neighbors list.

◆ MapCylinderToCube()

template<class T , int VECSIZE>

void open3d::ml::impl::MapCylinderToCube	(	Eigen::Array< T, VECSIZE, 1 > &	x,
		Eigen::Array< T, VECSIZE, 1 > &	y,
		Eigen::Array< T, VECSIZE, 1 > &	z
	)

inline

Maps coordinates in a cylinder with radius 1 to a cube. The input and output range of the coordinates is [-1,1]. The cylinder axis is along z.

◆ MapSphereToCylinder()

template<class T , int VECSIZE>

void open3d::ml::impl::MapSphereToCylinder	(	Eigen::Array< T, VECSIZE, 1 > &	x,
		Eigen::Array< T, VECSIZE, 1 > &	y,
		Eigen::Array< T, VECSIZE, 1 > &	z
	)

inline

Maps coordinates in a sphere with radius 1 to a cylinder. The input and output range of the coordinates is [-1,1]. The cylinder axis is along z.

◆ MultiplyAndCopyColumns()

template<class T >

void open3d::ml::impl::MultiplyAndCopyColumns	(	const cudaStream_t &	stream,
		size_t	rows,
		size_t	cols,
		T *__restrict__	out_ptr,
		const T *const __restrict__	col_major_matrix,
		const T *const __restrict__	vector
	)

Multiplies each column with a scalar.

Parameters

out_ptr	Output pointer
col_major_matrix	Matrix with shape [rows, cols] in column major storage order.
vector	A vector with shape [cols].

◆ MultiplyColumns()

template<class T >

void open3d::ml::impl::MultiplyColumns	(	const cudaStream_t &	stream,
		size_t	rows,
		size_t	cols,
		T *__restrict__	col_major_matrix,
		const T *const __restrict__	vector
	)

Multiplies each column with a scalar in-place.

Parameters

col_major_matrix	Matrix with shape [rows, cols] in column major storage order.
vector	A vector with shape [cols].

◆ RaggedToDenseCPU()

template<class T >

void open3d::ml::impl::RaggedToDenseCPU	(	const T *const	values,
		const int64_t *const	row_splits,
		const size_t	row_splits_size,
		const size_t	out_col_size,
		const T *const	default_value,
		const size_t	default_value_size,
		T *	out_values
	)

Creates a dense tensor from a ragged tensor.

Example where each value has size 2: values = [[0,0],[1,1],[2,2],[3,3],[4,4]] row_splits = [0,2,5] out_col_size=3 default_value=[-1,-1] default_value_size = 2

will return

out_values = [[[0,0],[1,1],[-1,-1]], [[2,2],[3,3],[4,4]]]

Parameters

values	Linear memory with all values.
row_splits	Defines the start and end of each entry in the ragged tensor. This is an exclusive prefix sum with 0 as the first element and the length of all values as the last element.
row_splits_size	The length of the row_splits vector.
out_col_size	The output column size. This is the second dim of the dense output tensor.
default_value	The default value to use if there are not enough values for filling the row.
default_value_size	The size of the default value.
out_values	This is the output array. The size of the array must be [row_splits_size-1, out_col_size, default_value_size].

◆ ReduceSubarraysSumCPU()

template<class T >

void open3d::ml::impl::ReduceSubarraysSumCPU	(	const T *const	values,
		const size_t	values_size,
		const int64_t *const	row_splits,
		const size_t	num_arrays,
		T *	out_sums
	)

Reduces subarrays in linear memory with the sum operation. The sum for empty subarrays is 0.

Parameters

values	The linear array with all values
values_size	Number of elements of `values`
row_splits	Defines the start and end of each subarray. This is an exclusive prefix sum with 0 as the first element and the length of `values` as last element. The size is `num_arrays` + 1
num_arrays	The number of subarrays
out_sums	The preallocated output array with size `num_arrays`

◆ SparseConvBackpropFilterCPU()

template<class TFeat , class TOut , class TIndex , class TKernelIndex >

void open3d::ml::impl::SparseConvBackpropFilterCPU	(	TOut *	filter_backprop,
		const std::vector< int > &	filter_dims,
		size_t	num_out,
		size_t	num_inp,
		const TFeat *	inp_features,
		const TFeat *	inp_importance,
		const TIndex *	neighbors_index,
		const TKernelIndex *	neighbors_kernel_index,
		const TFeat *	neighbors_importance,
		const int64_t *	neighbors_row_splits,
		const TFeat *	out_features_gradient,
		bool	normalize
	)

Computes the backprop for the filter of a sparse convolution.

Parameters

temp	Pointer to temporary memory. If nullptr then the required size of temporary memory will be written to `temp_size` and no work is done. This function can make use of more memory and returns the maximum size that can be used in max_temp_size.
temp_size	The size of the temporary memory in bytes. This is used as an output if temp is nullptr and returns the minimum temp size required.
max_temp_size	This is used as an output if temp is nullptr and returns the maximum temp size that can be used.
texture_alignment	The texture alignment in bytes. This is used for allocating segments within the temporary memory.
filter_backrop	Output array for the computed filter gradient with shape [depth,height,width, inp channels, out channels]
filter_dims	The sizes of the filter dimensions. The size of filter_dims must be >=3. The order is [num kernel elements, inp channels, out channels].
num_out	The number of output points.
num_inp	The number of input points.
inp_features	The input features with shape [num_inp, in_channels].
inp_importance	Optional importance for each input point with shape [num_inp]. Set to null to disable.
neighbors_index	The array with lists of neighbors for each output point. The start and end of each sublist is defined by `neighbors_row_splits`.
neighbors_kernel_index	Defines which kernel element to use for each neighbor. This array has the same length as `neighbors_index`.
neighbors_importance	Optional importance for each entry in `neighbors_index`. Set to null to disable.
neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `neighbors_index`. The size of the array is `num_out` + 1.
out_features_gradient	The gradient signal from the features.
normalize	If true then the output features are normalized either by the number of points (neighbors_importance is null) or by the sum of the respective values in neighbors_importance.

◆ SparseConvComputeFeaturesCPU()

template<class TFeat , class TOut , class TIndex , class TKernelIndex >

void open3d::ml::impl::SparseConvComputeFeaturesCPU	(	TOut *	out_features,
		const std::vector< int > &	filter_dims,
		const TFeat *	filter,
		size_t	num_out,
		size_t	num_inp,
		const TFeat *	inp_features,
		const TFeat *	inp_importance,
		size_t	neighbors_index_size,
		const TIndex *	neighbors_index,
		const TKernelIndex *	neighbors_kernel_index,
		const TFeat *	neighbors_importance,
		const int64_t *	neighbors_row_splits,
		bool	normalize
	)

Computes the output features of a sparse convolution.

Parameters

out_features	Output array for the computed features with shape [num_out, out channels]
filter_dims	The sizes of the filter dimensions. The size of filter_dims must be >=3. The order is [num kernel elements, inp channels, out channels].
filter	Pointer to the filter values.
num_out	The number of output points.
num_inp	The number of input points.
inp_features	The input features with shape [num_inp, in_channels].
inp_importance	Optional importance for each input point with shape [num_inp]. Set to null to disable.
neighbors_index_size	The size of the neighbors_index array.
neighbors_index	The array with lists of neighbors for each output point. The start and end of each sublist is defined by `neighbors_row_splits`.
neighbors_kernel_index	Defines which kernel element to use for each neighbor. This array has the same length as `neighbors_index`.
neighbors_importance	Array of the same length as `neighbors_importance`. Defines which of the kernel elements to use in the matrix multiplication.
neighbors_importance	Optional importance for each entry in `neighbors_index`. Set to null to disable.
neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `neighbors_index`. The size of the array is `num_out` + 1.
normalize	If true then the result is normalized either by the number of points (neighbors_importance is null) or by the sum of the respective values in neighbors_importance.

◆ SparseConvTransposeBackpropFilterCPU()

template<class TFeat , class TOut , class TIndex , class TKernelIndex >

void open3d::ml::impl::SparseConvTransposeBackpropFilterCPU	(	TOut *	filter_backprop,
		const std::vector< int > &	filter_dims,
		size_t	num_out,
		const TFeat *	out_importance,
		size_t	num_inp,
		const TFeat *	inp_features,
		const TFeat *	inp_neighbors_importance_sum,
		const int64_t *	inp_neighbors_row_splits,
		const TIndex *	neighbors_index,
		const TKernelIndex *	neighbors_kernel_index,
		const TFeat *	neighbors_importance,
		const int64_t *	neighbors_row_splits,
		const TFeat *	out_features_gradient,
		bool	normalize
	)

Computes the backprop for the filter of a transpose sparse convolution.

Parameters

filter_backrop	Output array for the computed filter gradient with shape [depth,height,width, inp channels, out channels]
filter_dims	The sizes of the filter dimensions. The size of filter_dims must be >=3. The order is [num kernel elements, inp channels, out channels].
filter	Pointer to the filter values.
num_out	The number of output points.
out_importance	Optional importance for each output point with shape [num_out]. Set to null to disable.
num_inp	The number of input points.
inp_features	The input features with shape [num_inp, in_channels].
inp_neighbors_importance_sum	The sum of the neighbors_importance values for each input with shape [num_inp].
inp_neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `inp_neighbors_index`. The size of the array is `num_inp` + 1.
neighbors_index_size	The size of the neighbors_index array.
neighbors_index	The array with lists of neighbors for each output point. The start and end of each sublist is defined by `neighbors_row_splits`.
neighbors_kernel_index	Defines which kernel element to use for each neighbor. This array has the same length as `neighbors_index`.
neighbors_importance	Optional importance for each entry in `neighbors_index`. Set to null to disable.
neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `neighbors_index`. The size of the array is `num_out` + 1.
out_features_gradient	The gradient from the features with shape [num_out, out_channels]
normalize	If true then the result is normalized either by the number of points (neighbors_importance is null) or by the sum of the respective values in neighbors_importance.

◆ SparseConvTransposeComputeFeaturesCPU()

template<class TFeat , class TOut , class TIndex , class TKernelIndex >

void open3d::ml::impl::SparseConvTransposeComputeFeaturesCPU	(	TOut *	out_features,
		const std::vector< int > &	filter_dims,
		const TFeat *	filter,
		size_t	num_out,
		const TFeat *	out_importance,
		size_t	num_inp,
		const TFeat *	inp_features,
		const TFeat *	inp_neighbors_importance_sum,
		const int64_t *	inp_neighbors_row_splits,
		const TIndex *	neighbor_index,
		const TKernelIndex *	neighbors_kernel_index,
		const TFeat *	neighbor_importance,
		const int64_t *	neighbors_row_splits,
		bool	normalize
	)

Computes the output features of a transpose sparse convolution.

Parameters

out_features	Output array for the computed features with shape [num_out, out channels]
filter_dims	The sizes of the filter dimensions. The size of filter_dims must be >=3. The order is [num kernel elements, inp channels, out channels].
filter	Pointer to the filter values.
num_out	The number of output points.
out_importance	Optional importance for each output point with shape [num_out]. Set to null to disable.
num_inp	The number of input points.
inp_features	The input features with shape [num_inp, in_channels].
inp_neighbors_importance_sum	The sum of the neighbors_importance values for each input with shape [num_inp].
inp_neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `inp_neighbors_index`. The size of the array is `num_inp` + 1.
neighbors_index	The array with lists of neighbors for each output point. The start and end of each sublist is defined by `neighbors_row_splits`.
neighbors_kernel_index	Defines which kernel element to use for each neighbor. This array has the same length as `neighbors_index`.
neighbors_importance	Optional importance for each entry in `neighbors_index`. Set to null to disable.
neighbors_row_splits	The prefix sum which defines the start and end of the sublists in `neighbors_index`. The size of the array is `num_out` + 1.
normalize	If true then the result is normalized either by the number of points (neighbors_importance is null) or by the sum of the respective values in neighbors_importance.

◆ SpatialHash() [1/2]

HOST_DEVICE size_t open3d::ml::impl::SpatialHash ( const utility::MiniVec< int, 3 > & xyz )

inline

◆ SpatialHash() [2/2]

HOST_DEVICE size_t open3d::ml::impl::SpatialHash	(	int	x,
		int	y,
		int	z
	)

inline

Spatial hashing function for integer coordinates.

◆ VoxelizeCPU()

template<class T , int NDIM, class OUTPUT_ALLOCATOR >

void open3d::ml::impl::VoxelizeCPU	(	const size_t	num_points,
		const T *const	points,
		const size_t	batch_size,
		const int64_t *const	row_splits,
		const T *const	voxel_size,
		const T *const	points_range_min,
		const T *const	points_range_max,
		const int64_t	max_points_per_voxel,
		const int64_t	max_voxels,
		OUTPUT_ALLOCATOR &	output_allocator
	)

This function voxelizes a point cloud. The function returns the integer coordinates of the voxels that contain points and a compact list of the indices that associate the voxels to the points.

Template Parameters

T	Floating-point data type for the point positions.
NDIM	The number of dimensions of the points.
OUTPUT_ALLOCATOR	Type of the output_allocator. See `output_allocator` for more information.

Parameters

num_points	The number of points.
points	Array with the point positions. The shape is [num_points,NDIM].
batch_size	The batch size of points.
row_splits	row_splits for defining batches.
voxel_size	The edge lengths of the voxel. The shape is [NDIM]. This pointer points to host memory!
points_range_min	The lower bound of the domain to be voxelized. The shape is [NDIM]. This pointer points to host memory!
points_range_max	The upper bound of the domain to be voxelized. The shape is [NDIM]. This pointer points to host memory!
max_points_per_voxel	This parameter limits the number of points that are recorderd for each voxel.
max_voxels	This parameter limits the number of voxels that will be generated.
output_allocator	An object that implements functions for allocating the output arrays. The object must implement functions AllocVoxelCoords(int32_t ptr, int64_t rows, int64_t cols), AllocVoxelPointIndices(int64_t ptr, int64_t size), AllocVoxelPointRowSplits(int64_t ptr, int64_t size) and AllocVoxelBatchSplits(int64_t ptr, int64_t size). All functions should allocate memory and return a pointer to that memory in ptr. The arguments size, rows, and cols define the size of the array as the number of elements. All functions must accept zero size arguments. In this case ptr does not need to be set.

◆ VoxelPooling()

template<class TReal , class TFeat , class OUTPUT_ALLOCATOR >

void open3d::ml::impl::VoxelPooling	(	size_t	num_inp,
		const TReal *const	inp_positions,
		int	in_channels,
		const TFeat *	inp_features,
		TReal	voxel_size,
		OUTPUT_ALLOCATOR &	output_allocator,
		AccumulationFn	position_fn,
		AccumulationFn	feature_fn
	)

Pooling operation for point clouds. Aggregates points that are inside the same voxel.

Template Parameters

TReal	Scalar type for point positions.
TFeat	Scalar type for the features.
OUTPUT_ALLOCATOR	Type of the output_allocator. See `output_allocator` for more information.

Parameters

num_inp	The number of input points.
inp_positions	Array with 3D point positions.
in_channels	The number of input feature channels.
inp_features	The array with the point features. The shape is [num_inp, in_channels].
voxel_size	The voxel size (voxel edge length) used for pooling.
output_allocator	An object that implements functions for allocating the output arrays. The object must implement functions AllocPooledPositions(TReal ptr, size_t num) and AllocPooledFeatures(TFeat ptr, size_t num, channels), with 'num' as the number of output points and 'channels' as `in_channels` . Both functions should allocate memory and return a pointer to that memory in ptr. Both functions must accept the argument num==0. In this case ptr does not need to be set. Note that AllocPooledPositions must allocate memory for num3sizeof(TReal) bytes.
position_fn	Defines how the new point positions will be computed. AVERAGE computes the center of gravity for the points within one voxel. NEAREST_NEIGHBOR selects the point closest to the voxel center. CENTER uses the voxel center for the position of the generated point.
feature_fn	Defines how the new features will be computed. AVERAGE computes the average feature vector. NEAREST_NEIGHBOR selects the feature vector of the point closest to the voxel center. MAX uses the maximum feature among all points within the voxel.

◆ VoxelPoolingBackprop()

template<class TReal , class TFeat >

void open3d::ml::impl::VoxelPoolingBackprop	(	TFeat *	features_backprop,
		size_t	num_inp,
		const TReal *const	inp_positions,
		int	in_channels,
		const TFeat *const	inp_features,
		size_t	num_pooled,
		const TReal *const	pooled_positions,
		const TFeat *const	pooled_features_gradient,
		TReal	voxel_size,
		AccumulationFn	position_fn,
		AccumulationFn	feature_fn
	)

Backpropagation to the features for VoxelPooling.

Parameters

features_backprop	The output array with the gradients for the features.
num_pooled	The number of points after pooling with `VoxelPooling`.
pooled_positions	Array with the 3D positions after pooling.
pooled_features_gradient	Array with the point features after pooling.

See VoxelPooling for the description of the remaining parameters.

Data Structures

Enumerations

Functions

Enumeration Type Documentation

◆ AccumulationFn

◆ CoordinateMapping

◆ InterpolationMode

◆ Metric

Function Documentation

◆ _CConvBackropFilterCPU()

◆ _CConvComputeFeaturesCPU()

◆ _CConvTransposeBackpropFilterCPU()

◆ _CConvTransposeComputeFeaturesCPU()

◆ _SparseConvBackropFilterCPU()

◆ _SparseConvComputeFeaturesCPU()

◆ _SparseConvTransposeBackpropFilterCPU()

◆ _SparseConvTransposeComputeFeaturesCPU()

◆ _VoxelPooling()

◆ _VoxelPoolingBackprop()

◆ CConvBackpropFilterCPU()

◆ CConvComputeFeaturesCPU()

◆ CConvTransposeBackpropFilterCPU()

◆ CConvTransposeComputeFeaturesCPU()

◆ CheckVoxelSize()

◆ ComputeFilterCoordinates()

◆ ComputeVoxelIndex() [1/2]

◆ ComputeVoxelIndex() [2/2]

◆ FillColumn() [1/2]

◆ FillColumn() [2/2]

◆ FillColumnTranspose() [1/2]

◆ FillColumnTranspose() [2/2]

◆ InvertNeighborsListCPU()

◆ MapCylinderToCube()

◆ MapSphereToCylinder()

◆ MultiplyAndCopyColumns()

◆ MultiplyColumns()

◆ RaggedToDenseCPU()

◆ ReduceSubarraysSumCPU()

◆ SparseConvBackpropFilterCPU()

◆ SparseConvComputeFeaturesCPU()

◆ SparseConvTransposeBackpropFilterCPU()

◆ SparseConvTransposeComputeFeaturesCPU()

◆ SpatialHash() [1/2]

◆ SpatialHash() [2/2]

◆ VoxelizeCPU()

◆ VoxelPooling()

◆ VoxelPoolingBackprop()