Global-Aware Registration of Less-Overlap RGB-D Scans

Che Sun, Yunde Jia, Yi Guo, Yuwei Wu

Abstract: We propose a novel method of registering less-overlap RGB-D scans. Our method learns global information of a scene to construct a panorama, and aligns RGB-D scans to the panorama to perform registration. Different from existing methods that use local feature points to register less-overlap RGB-D scans and mismatch too much, we use global information to guide the registration, thereby allevi-ating the mismatching problem by preserving global consis-tency of alignments. To this end, we build a scene inference network to construct the panorama representing global in-formation. We introduce a reinforcement learning strategy to iteratively align RGB-D scans with the panorama and re-fine the panorama representation, which reduces the noise of global information and preserves global consistency of both geometric and photometric alignments. Experimental results on benchmark datasets including SUNCG, Matterport, and ScanNet show the superiority of our method.