open3d.ml.torch.models.RandLANet

class open3d.ml.torch.models.RandLANet(name='RandLANet', num_neighbors=16, num_layers=4, num_points=45056, num_classes=19, ignored_label_inds=[0], sub_sampling_ratio=[4, 4, 4, 4], in_channels=3, dim_features=8, dim_output=[16, 64, 128, 256], grid_size=0.06, batcher='DefaultBatcher', ckpt_path=None, augment={}, **kwargs)

Class defining RandLANet, a Semantic Segmentation model. Based on the architecture from the paper RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds.

RandLA-Net is an efficient and lightweight neural architecture which directly infer per-point semantics for large-scale point clouds. The key approach is to use random point sampling instead of more complex point selection approaches. Although remarkably computation and memory efficient, random sampling can discard key features by chance. To overcome this, we introduce a novel local feature aggregation module to progressively increase the receptive field for each 3D point, thereby effectively preserving geometric details.

Architecture

References

https://github.com/QingyongHu/RandLA-Net

__init__(name='RandLANet', num_neighbors=16, num_layers=4, num_points=45056, num_classes=19, ignored_label_inds=[0], sub_sampling_ratio=[4, 4, 4, 4], in_channels=3, dim_features=8, dim_output=[16, 64, 128, 256], grid_size=0.06, batcher='DefaultBatcher', ckpt_path=None, augment={}, **kwargs)

Initialize.

Parameters:

• cfg (cfg object or str) – cfg object or path to cfg file

• dataset_path (str) – path to the dataset

• **kwargs (dict) – Dict of args

forward(inputs)

Forward pass for RandLANet

Parameters:

inputs – torch.Tensor, shape (B, N, d_in) input points

Returns

torch.Tensor, shape (B, num_classes, N)

segmentation scores for each point

get_loss(Loss, results, inputs, device)

Calculate the loss on output of the model.

Parameters:

• Loss – Object of type SemSegLoss.

• results – Output of the model (B, N, C).

• inputs – Input of the model.

• device – device(cpu or cuda).

Returns:

Returns loss, labels and scores.

get_optimizer(cfg_pipeline)

Returns an optimizer object for the model.

Parameters:

cfg_pipeline – A Config object with the configuration of the pipeline.

Returns:

Returns a new optimizer object.

inference_begin(data)

Function called right before running inference.

Parameters:

data – A data from the dataset.

inference_end(inputs, results)

This function is called after the inference.

This function can be implemented to apply post-processing on the network outputs.

Parameters:

results – The model outputs as returned by the call() function. Post-processing is applied on this object.

Returns:

Returns True if the inference is complete and otherwise False. Returning False can be used to implement inference for large point clouds which require multiple passes.

inference_preprocess()

This function prepares the inputs for the model.

Returns:

The inputs to be consumed by the call() function of the model.

static nearest_interpolation(feature, interp_idx)

Parameters:

• feature – [B, d, N] input features matrix

• interp_idx – [B, up_num_points, 1] nearest neighbour index

Returns:

[B, up_num_points, d] interpolated features matrix

preprocess(data, attr)

Data preprocessing function.

This function is called before training to preprocess the data from a dataset.

Parameters:

• data – A sample from the dataset.

• attr – The corresponding attributes.

Returns:

Returns the preprocessed data

static random_sample(feature, pool_idx)

Parameters:

• feature – [B, d, N, 1] input features matrix

• pool_idx – [B, N’, max_num] N’ < N, N’ is the selected position after pooling

Returns:

pool_features = [B, N’, d] pooled features matrix

transform(data, attr, min_possibility_idx=None)

Transform function for the point cloud and features.

Parameters:

cfg_pipeline – config file for pipeline.

update_probs(inputs, results, test_probs)

Update test probabilities with probs from current tested patch.

Parameters:

• inputs – input to the model.

• results – output of the model.

• test_probs – probabilities for whole pointcloud

Returns:

updated probabilities

training: bool