src.model.deeplearn.dlrun.point_net_post_processor

Classes

PointNetPostProcessor(pnet_preproc, **kwargs)

class src.model.deeplearn.dlrun.point_net_post_processor.PointNetPostProcessor(pnet_preproc, **kwargs)
Author:

Alberto M. Esmoris Pena

Postprocess the output of a PointNet neural network to transform it to the expected output format.

Variables:

pnet_preproc (PointNetPreProcessor) – The preprocessor that generated the input for the model which output must be handled by the post-processor.

__init__(pnet_preproc, **kwargs)

Initialization/instantiation of a PointNet post-processor.

Parameters:

  • pnet_preproc (PointNetPreProcessor) – The pre-processor associated to the model which output must be handled by the post-processor.

  • kwargs – The key-word arguments for the PointNetPostProcessor.

__call__(inputs, reducer=None)

Executes the post-processing logic.

Parameters:

  • inputs (dict) – A key-word input where the key “X” gives the coordinates of the points in the original point cloud. Also, the key “z” gives the predictions computed on a receptive field of \(R\) points that must be propagated back to the \(m\) points of the original point cloud.

  • reducer (PredictionReducer) – The prediction reducer for the post-processor, if any.

Returns:

The \(m\) point-wise predictions.