将3D点云实例分割与多模态理解结合，支持零样本检索。
SegVec3D: A Method for Vector Embedding of 3D Objects Oriented Towards Robot manipulation
- 基于注意力与对比聚类实现无监督实例分割
- 在共享语义空间中对齐3D数据与自然语言查询
- 仅需少量标注即可部署，适合机器人操作场景
我们提出SegVec3D，一种新型3D点云实例分割框架，融合注意力机制、嵌入学习与跨模态对齐。该方法构建分层特征提取器以增强几何结构建模，并通过对比聚类实现无监督实例分割。进一步地，将3D数据与自然语言查询对齐至共享语义空间，支持零样本检索。相较于Mask3D和ULIP等近期方法，本方法在最小监督下统一实例分割与多模态理解，具备良好实用部署性。
原文摘要 · Abstract (English)
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.
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