SE3ET: 低重複点群位置合わせのためのSE(3)同変トランスフォーマー
SE3ET: SE(3)-Equivariant Transformer for Low-Overlap Point Cloud Registration
3D点群の同変学習を活用し、大きな変換や低重複率でも頑健に部分点群を位置合わせするSE(3)同変フレームワークを提案した。
著者: Chien Erh Lin, Minghan Zhu, Maani Ghaffari
分類: cs.RO
原文アブストラクト
Partial point cloud registration is a challenging problem in robotics, especially when the robot undergoes a large transformation, causing a significant initial pose error and a low overlap between measurements. This work proposes exploiting equivariant learning from 3D point clouds to improve registration robustness. We propose SE3ET, an SE(3)-equivariant registration framework that employs equivariant point convolution and equivariant transformer designs to learn expressive and robust geometric features. We tested the proposed registration method on indoor and outdoor benchmarks where the point clouds are under arbitrary transformations and low overlapping ratios. We also provide generalization tests and run-time performance.
