Authors: Ziyu Guo, Renrui Zhang, Xiangyang Zhu, Chengzhuo Tong, Peng Gao, Chunyuan Li, Pheng-Ann Heng
Abstract: We introduce SAM2Point, a preliminary exploration adapting Segment Anything
Model 2 (SAM 2) for zero-shot and promptable 3D segmentation. SAM2Point
interprets any 3D data as a series of multi-directional videos, and leverages
SAM 2 for 3D-space segmentation, without further training or 2D-3D projection.
Our framework supports various prompt types, including 3D points, boxes, and
masks, and can generalize across diverse scenarios, such as 3D objects, indoor
scenes, outdoor environments, and raw sparse LiDAR. Demonstrations on multiple
3D datasets, e.g., Objaverse, S3DIS, ScanNet, Semantic3D, and KITTI, highlight
the robust generalization capabilities of SAM2Point. To our best knowledge, we
present the most faithful implementation of SAM in 3D, which may serve as a
starting point for future research in promptable 3D segmentation. Online Demo:
https://huggingface.co/spaces/ZiyuG/SAM2Point . Code:
https://github.com/ZiyuGuo99/SAM2Point .
Source: http://arxiv.org/abs/2408.16768v1
