open3d.ml.torch.ops.sparse_conv_transpose

open3d.ml.torch.ops.sparse_conv_transpose(filters, out_importance, inp_features, inp_neighbors_index, inp_neighbors_importance_sum, inp_neighbors_row_splits, neighbors_index, neighbors_kernel_index, neighbors_importance, neighbors_row_splits, normalize=False, max_temp_mem_MB=64)

Sparse tranpose convolution of two pointclouds.

normalize:

If True the input feature values will be normalized using ‘inp_neighbors_importance_sum’.

max_temp_mem_MB:

Defines the maximum temporary memory in megabytes to be used for the GPU implementation. More memory means fewer kernel invocations. Note that the a minimum amount of temp memory will always be allocated even if this variable is set to 0.

filters:

The filter parameters. The shape of the filter is [depth, height, width, in_ch, out_ch]. The dimensions ‘depth’, ‘height’, ‘width’ define the spatial resolution of the filter. The spatial size of the filter is defined by the parameter ‘extents’.

out_importance:

An optional scalar importance for each output point. The output features of each point will be multiplied with the corresponding value. The shape is [num input points]. Use a zero length Tensor to disable.

inp_features:

A 2D tensor which stores a feature vector for each input point.

inp_neighbors_index:

The inp_neighbors_index stores a list of indices of neighbors for each input point as nested lists. The start and end of each list can be computed using ‘inp_neighbors_row_splits’.

inp_neighbors_importance_sum:

1D tensor of the same length as ‘inp_features’ or zero length if neighbors_importance is empty. This is the sum of the values in ‘neighbors_importance’ for each input point.

inp_neighbors_row_splits:

The exclusive prefix sum of the neighbor count for the input points including the total neighbor count as the last element. The size of this array is the number of input points + 1.

neighbors_index:

The neighbors_index stores a list of indices of neighbors for each output point as nested lists. The start and end of each list can be computed using ‘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:

Tensor of the same shape as ‘neighbors_index’ with a scalar value that is used to scale the features of each neighbor.

neighbors_row_splits:

The exclusive prefix sum of the neighbor count for the output points including the total neighbor count as the last element. The size of this array is the number of output points + 1.

output_type: The type for the output.

out_features:

A Tensor with the output feature vectors for each output point.