Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance
This addresses the problem of robust generalization in pointcloud segmentation for applications like robotics or 3D scene understanding, though it appears incremental as it builds on existing equivariance concepts.
The paper tackles the challenge of capturing inter-part transformations in complex systems like articulated objects for pointcloud segmentation, presenting Banana, a Banach fixed-point network that achieves strong generalization under such transformations.
Equivariance has gained strong interest as a desirable network property that inherently ensures robust generalization. However, when dealing with complex systems such as articulated objects or multi-object scenes, effectively capturing inter-part transformations poses a challenge, as it becomes entangled with the overall structure and local transformations. The interdependence of part assignment and per-part group action necessitates a novel equivariance formulation that allows for their co-evolution. In this paper, we present Banana, a Banach fixed-point network for equivariant segmentation with inter-part equivariance by construction. Our key insight is to iteratively solve a fixed-point problem, where point-part assignment labels and per-part SE(3)-equivariance co-evolve simultaneously. We provide theoretical derivations of both per-step equivariance and global convergence, which induces an equivariant final convergent state. Our formulation naturally provides a strict definition of inter-part equivariance that generalizes to unseen inter-part configurations. Through experiments conducted on both articulated objects and multi-object scans, we demonstrate the efficacy of our approach in achieving strong generalization under inter-part transformations, even when confronted with substantial changes in pointcloud geometry and topology.