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.
@article{arxiv.2507.09459,
title = {SegVec3D: A Method for Vector Embedding of 3D Objects Oriented Towards Robot manipulation},
author = {Zhihan Kang and Boyu Wang},
journal= {arXiv preprint arXiv:2507.09459},
year = {2025}
}