Summary of Region-transformer: Self-attention Region Based Class-agnostic Point Cloud Segmentation, by Dipesh Gyawali et al.
Region-Transformer: Self-Attention Region Based Class-Agnostic Point Cloud Segmentation
by Dipesh Gyawali, Jian Zhang, BB Karki
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 proposed Region-Transformer model is a novel region-based transformer designed for class-agnostic point cloud segmentation. It uses a region-growth approach and self-attention mechanism to iteratively expand or contract regions by adding or removing points. The model is trained on simulated point clouds with instance labels only, avoiding semantic labels. By utilizing attention-based networks and self-attention mechanisms, the Region-Transformer outperforms previous class-agnostic and class-specific methods in indoor datasets regarding clustering metrics. It generalizes well to large-scale scenes and has key advantages such as capturing long-range dependencies through self-attention, avoiding the need for semantic labels during training, and applicability to a variable number of objects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Point cloud segmentation is important for understanding environments of specific structures and objects. Researchers have proposed different methods, including class-specific and class-agnostic approaches. The new Region-Transformer model uses a region-growth approach with self-attention mechanism to segment point clouds without needing semantic labels during training. This makes it flexible and useful for applications like robotics, digital twinning, and autonomous vehicles. |
Keywords
* Artificial intelligence * Attention * Clustering * Self attention * Transformer
