MuSCLe-Reg: Multi-Scale Contextual Embedding and Local Correspondence Rectification for Robust Two-Stage Point Cloud Registration

Abstract

Algorithm of outlier removal for learning-based 3D point cloud registration is usually regarded as a classification problem. The core for this to be successful is to learn the discriminative inlier/outlier feature representations. This letter proposes a two-stage efficient network (MuSCLe-Reg) with multi-scale local feature fusion embedding. Specifically, we have designed a two-stage registration architecture. Firstly, we construct a graph topology feature consisting of correspondences and their feature neighborhoods. Then, the feature representation of correspondences was enhanced through multi-scale feature mapping and fusion (MSF). In addition, we propose a local correspondence rectification strategy (LCR) based on feature neighbors to evaluate initial candidates and generate higher-quality correspondences. The experimental results on various datasets show that compared with existing learning-based algorithms, this network has better accuracy and stronger generalization ability. Especially, in robustness testing across varying numbers of correspondences in the 3DLoMatch dataset, the algorithm demonstrated superior estimation performance compared to current state-of-the-art registration techniques.