ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition

arXiv:2403.14619v1 Announce Type: new
Abstract: 3D decomposition/segmentation still remains a challenge as large-scale 3D annotated data is not readily available. Contemporary approaches typically leverage 2D machine-generated segments, integrating them for 3D consistency. While the majority of these methods are based on NeRFs, they face a potential weakness that the instance/semantic embedding features derive from independent MLPs, thus preventing the segmentation network from learning the geometric details of the objects directly through radiance and density. In this paper, we propose ClusteringSDF, …

abstract annotated data arxiv challenge cs.cv data embedding face features generated instance machine scale segmentation semantic them type