Point clouds provide a flexible 3D data structure, bypassing the complexity of meshes. Typically, researchers convert point cloud data into 3D voxel grids for deep learning models, but this introduces artifacts and increased data size. PointNet offers a direct approach by utilizing raw point cloud data. This model leverages point data's permutation invariance via a symmetric MaxPooling function, enhancing efficiency. The architecture supports both classification and segmentation tasks, handling 3D transformations effectively.

Implementation in MQL5 involves a new PointNet class, leveraging convolutional layers for feature extraction. This facilitates efficient point cloud classification within trading algorithms, integrating environmental state analysis.

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PointNet++ extends the original PointNet by incorporating local structure awareness, addressing a key limitation in the original design. It partitions point sets into overlapping local regions based on Euclidean distances, allowing for enhanced feature extraction similar to convolutional networks. Multiple abstraction levels allow for progressively capturing local details, and density-adaptive layers ensure effective handling of non-uniform point densities.

Implementation in MQL5 involves creating a local subsampling layer by extending the OpenCL program. This includes the CalcDistance kernel for calculating point distances in feature space and normalizing these distances to define adaptive receptive fields. The approach leverages efficient centroid selection via farthest-point sampling. The methodology circumvents challenges in local displacement calculatio...

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