Summary of Point-sam: Promptable 3d Segmentation Model For Point Clouds, by Yuchen Zhou et al.
Point-SAM: Promptable 3D Segmentation Model for Point Clouds
by Yuchen Zhou, Jiayuan Gu, Tung Yen Chiang, Fanbo Xiang, Hao Su
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The Segment Anything Model (SAM) has successfully advanced 2D foundation models for image segmentation, but achieving similar success in 3D models remains a challenge due to issues such as non-unified data formats, poor model scalability, and the scarcity of labeled data with diverse masks. To address this, Point-SAM is proposed, focusing on point clouds. The efficient transformer-based architecture is tailored for point clouds, extending SAM to the 3D domain. By distilling rich knowledge from 2D SAM for training, Point-SAM generates part-level and object-level pseudo-labels at scale using a data engine. This model outperforms state-of-the-art 3D segmentation models on several indoor and outdoor benchmarks and demonstrates applications such as interactive 3D annotation and zero-shot 3D instance proposal. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Point-SAM is a new 3D segmentation model that helps computers understand and label 3D images. Right now, it’s hard to make 3D models work well because there aren’t many labeled examples, the data isn’t organized, and the models can’t handle big amounts of information. The Point-SAM team created a new way to train their model using 2D images and labels, which helps it learn from a lot more data than before. This makes the model better at segmenting 3D images and allows it to be used in different applications like interactive 3D annotation. |
Keywords
» Artificial intelligence » Image segmentation » Sam » Transformer » Zero shot
