Summary of Sam2point: Segment Any 3d As Videos in Zero-shot and Promptable Manners, by Ziyu Guo et al.
SAM2Point: Segment Any 3D as Videos in Zero-shot and Promptable Manners
by Ziyu Guo, Renrui Zhang, Xiangyang Zhu, Chengzhuo Tong, Peng Gao, Chunyuan Li, Pheng-Ann Heng
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary SAM2Point is a preliminary exploration that adapts Segment Anything Model 2 (SAM 2) for zero-shot and promptable 3D segmentation. The framework interprets any 3D data as a series of multi-directional videos, leveraging SAM 2 for 3D-space segmentation without further training or 2D-3D projection. It 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 like Objaverse, S3DIS, ScanNet, Semantic3D, and KITTI highlight the robust generalization capabilities of SAM2Point. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SAM2Point is a new way to look at 3D data. It takes any 3D information and turns it into a series of videos that can be analyzed in different directions. This helps with tasks like separating objects from backgrounds. The system works well with different types of prompts, such as points, boxes, or masks, and can work on many kinds of 3D scenes, like buildings, outdoors, or even raw data from LiDAR sensors. |
Keywords
» Artificial intelligence » Generalization » Prompt » Sam » Zero shot
