A Unified Non-Parametric and Interpretable Point Cloud Analysis via t-FCW Graph Representation
For researchers in 3D point cloud analysis, this work offers a fast, interpretable, and lightweight alternative or complement to existing deep learning models.
The paper introduces an empowered transposed Fully Connected Weighted (t-FCW) graph representation for point cloud analysis, achieving ModelNet40 classification in ~7 seconds on an RTX A5000 GPU while providing interpretability and serving as a standalone baseline or plug-in.
We introduce an empowered transposed Fully Connected Weighted (t-FCW) graph representation to embed point clouds into a metric space. While original t-FCW has shown promising results for point cloud classification, the reasons behind its effectiveness and its broader applicability remained unclear. In this work, we analyze the properties that make the empowered and original t-FCW effective and design a network that uses the empowered t-FCW exclusively as feature extractors. From an interpretability perspective, we build memory banks for classification, part segmentation, and semantic segmentation using the empowered t-FCW. Our analysis reveals that the empowered t-FCW inherits robustness from surface descriptors, provides interpretability through dimension-wise relations. These properties enable a highly efficient and interpretable network, which processes the ModelNet40 classification problem in approximately 7 seconds on an NVIDIA RTX A5000 GPU. Importantly, empowered t-FCW can function both as a lightweight standalone baseline and as a complementary plug-in to existing deep models.