Summary of Equi-gspr: Equivariant Se(3) Graph Network Model For Sparse Point Cloud Registration, by Xueyang Kang and Zhaoliang Luan and Kourosh Khoshelham and Bing Wang
Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration
by Xueyang Kang, Zhaoliang Luan, Kourosh Khoshelham, Bing Wang
First submitted to arxiv on: 8 Oct 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 In this paper, researchers aim to improve point cloud registration, a crucial step in 3D alignment and reconstruction. They propose a novel graph neural network (GNN) model that leverages rotation equivariance to learn effectively from data. The model consists of a descriptor module, equivariant graph layers, match similarity, and final regression layers. By utilizing sparsely sampled input points and initializing the descriptor with self-trained or pre-trained geometric feature descriptors, the model achieves state-of-the-art performance on the 3DMatch and KITTI datasets while maintaining relatively low complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem in computer vision called point cloud registration. It’s like trying to match two jigsaw puzzles that are slightly different. The researchers came up with a new way to do this using special kinds of neural networks called graph neural networks. These networks can learn from the data and make good predictions. They tested their idea on some real-world datasets and it worked really well. |
Keywords
» Artificial intelligence » Alignment » Gnn » Graph neural network » Regression
