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About this paper
Ahmed Baha Ben Jmaa, Faten Chaieb
The authors present LD-PointNet++, a method for classifying sparse 3D point clouds. It expands the radius-defined neighborhoods around sampled points, allowing local features to capture broader shape context. On ModelNet40, trained with 1,024 points and tested with 64, it achieves 90.10% overall accuracy, compared with 35.39% for PointNet++. This controlled test uses randomly subsampled CAD objects, not real sensor data, and does not isolate neighborhood expansion as the sole cause.
Summary by alphaXiv.
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