Summary of Driv3r: Learning Dense 4d Reconstruction For Autonomous Driving, by Xin Fei et al.
Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving
by Xin Fei, Wenzhao Zheng, Yueqi Duan, Wei Zhan, Masayoshi Tomizuka, Kurt Keutzer, Jiwen Lu
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Driv3R framework enables real-time 4D reconstruction for dynamic scenes, a crucial challenge in autonomous driving perception. By directly regressing per-frame point maps from multi-view image sequences using a DUSt3R-based approach, the method achieves streaming dense reconstruction while maintaining spatial relationships across sensors and dynamic temporal contexts. A 4D flow predictor identifies moving objects within the scene, directing network focus towards reconstructing these dynamic regions. The framework also aligns all per-frame pointmaps consistently to the world coordinate system without requiring optimization. Extensive experiments on the nuScenes dataset demonstrate Driv3R’s effectiveness in 4D dynamic scene reconstruction, outperforming previous frameworks with a 15x faster inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Driv3R is a new way for computers to build detailed 3D models of moving objects and scenes. This is important for self-driving cars to understand the world around them. The method uses many cameras at once to create a 3D model in real-time, which is faster than other methods that require more processing power. The system can also identify moving objects and focus on reconstructing those areas. This technology has been tested on a large dataset of scenes and performs better than previous methods. |
Keywords
» Artificial intelligence » Inference » Optimization
