MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Images

arXiv:2403.01517v1 Announce Type: new
Abstract: Recent learning methods for object pose estimation require resource-intensive training for each individual object instance or category, hampering their scalability in real applications when confronted with previously unseen objects. In this paper, we propose MatchU, a Fuse-Describe-Match strategy for 6D pose estimation from RGB-D images. MatchU is a generic approach that fuses 2D texture and 3D geometric cues for 6D pose prediction of unseen objects. We rely on learning geometric 3D descriptors that are rotation-invariant …

abstract applications arxiv cs.cv images instance match objects paper rgb-d scalability strategy training type