Discussing the article: "Neural Networks in Trading: Point Cloud Analysis (PointNet)"

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Check out the new article: Neural Networks in Trading: Point Cloud Analysis (PointNet).

Direct point cloud analysis avoids unnecessary data growth and improves the performance of models in classification and segmentation tasks. Such approaches demonstrate high performance and robustness to perturbations in the original data.

Point clouds are simple and unified structures that avoid combinatorial inconsistencies and complexities associated with meshes. Since point clouds do not have a conventional format, most researchers typically convert such datasets into regular <i0>3D</i0> voxel grids or image sets before passing them into a deep network architecture. However, this conversion makes the resulting data unnecessarily large and can introduce quantization artifacts, often obscuring the natural invariances of the data.

For this reason, some researchers have turned to an alternative representation of 3D geometry, using point clouds directly. Models operating with such raw data representations must account for the fact that a point cloud is merely a set of points and is invariant to permutations of its elements. This necessitates a certain degree of symmetrization in the model's computations.

One such solution was presented in the paper "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation". The model introduced in this work, named PointNet, is a unified architectural solution that directly takes a point cloud as input and outputs either class labels for the entire dataset or segmentation labels for individual points within the dataset.


Author: Dmitriy Gizlyk

 
Hello. Could you please send me your sample to try out? I'm not getting anything good.
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