Crossmodal few-shot 3d point cloud semantic segmentation

This paper introduces a cross-modal few-shot approach for 3D point cloud segmentation that uses labeled 2D images instead of 3D annotations. By converting 2D images to 3D format and employing a co-embedding network, the method achieves effective segmentation through prototype-based cosine similarity, performing competitively on benchmarks with minimal labeled 2D support.