A Unified Non-Parametric and Interpretable Point Cloud Analysis via t-FCW Graph Representation
4d ago
Quick Take
The empowered t-FCW graph representation enhances point cloud analysis with interpretability and efficiency.
Key Points
- Empowered t-FCW improves classification and segmentation tasks.
- Processes ModelNet40 classification in ~7 seconds on NVIDIA RTX A5000.
- Functions as a standalone baseline or a plug-in for deep models.
📖 Reader Mode
~2 min readAbstract: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.
| Comments: | Accepted for publication in IEEE Transactions on Multimedia |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM) |
| Cite as: | arXiv:2605.15475 [cs.CV] |
| (or arXiv:2605.15475v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15475 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bowie Liu [view email]
[v1]
Thu, 14 May 2026 23:30:27 UTC (744 KB)
— Originally published at arxiv.org
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