Summary of Eadreg: Probabilistic Correspondence Generation with Efficient Autoregressive Diffusion Model For Outdoor Point Cloud Registration, by Linrui Gong et al.
EADReg: Probabilistic Correspondence Generation with Efficient Autoregressive Diffusion Model for Outdoor Point Cloud Registration
by Linrui Gong, Jiuming Liu, Junyi Ma, Lihao Liu, Yaonan Wang, Hesheng Wang
First submitted to arxiv on: 22 Nov 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 A novel framework named EADReg is proposed for efficient and robust registration of LiDAR point clouds based on autoregressive diffusion models. The framework follows a coarse-to-fine registration paradigm, employing Bi-directional Gaussian Mixture Model (BGMM) to reject outlier points and obtain purified point cloud pairs. In the fine stage, an autoregressive process is used to generate robust point correspondences, which are then iteratively refined on upper layers. Despite common criticisms of diffusion-based methods regarding inference speed, EADReg achieves runtime comparable to convolutional-based methods. The proposed method demonstrates state-of-the-art performance on KITTI and NuScenes benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EADReg is a new way to match up point clouds from LiDAR sensors. This helps with registering points in challenging situations like outdoor environments where the data is sparse, irregular, and has many points. The method uses two stages: first, it removes noise and outlier points using something called Bi-directional Gaussian Mixture Model (BGMM). Then, it uses a special type of model called an autoregressive diffusion model to match up points in a more detailed way. This method is fast and works well on real-world data. |
Keywords
» Artificial intelligence » Autoregressive » Diffusion » Diffusion model » Inference » Mixture model
