SegVec3D: A Method for Vector Embedding of 3D Objects Oriented Towards Robot manipulation
It addresses 3D object segmentation for robot manipulation, offering a unified approach with practical deployability, but appears incremental as it builds on existing methods like Mask3D and ULIP.
The paper tackles 3D point cloud instance segmentation by proposing SegVec3D, a framework that integrates attention, embedding learning, and cross-modal alignment, achieving unsupervised instance segmentation and zero-shot retrieval with minimal supervision.
We propose SegVec3D, a novel framework for 3D point cloud instance segmentation that integrates attention mechanisms, embedding learning, and cross-modal alignment. The approach builds a hierarchical feature extractor to enhance geometric structure modeling and enables unsupervised instance segmentation via contrastive clustering. It further aligns 3D data with natural language queries in a shared semantic space, supporting zero-shot retrieval. Compared to recent methods like Mask3D and ULIP, our method uniquely unifies instance segmentation and multimodal understanding with minimal supervision and practical deployability.