Summary of Lidar-rt: Gaussian-based Ray Tracing For Dynamic Lidar Re-simulation, by Chenxu Zhou et al.
LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation
by Chenxu Zhou, Lvchang Fu, Sida Peng, Yunzhi Yan, Zhanhua Zhang, Yong Chen, Jiazhi Xia, Xiaowei Zhou
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 A novel framework, LiDAR-RT, is proposed to achieve real-time, physically accurate LiDAR re-simulation for dynamic driving scenarios. By integrating Gaussian primitives and hardware-accelerated ray tracing technology, the framework efficiently renders LiDAR views while modeling physical properties of LiDAR sensors. The approach supports realistic rendering with flexible scene editing operations and various sensor configurations. Experimental results demonstrate that LiDAR-RT outperforms state-of-the-art methods in terms of rendering quality and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Real-time LiDAR re-simulation is a challenge in dynamic driving scenarios. Scientists have developed new ways to combine neural radiance fields with physical modeling of LiDAR sensors, but these methods are limited by high computational demands in large-scale scenes. To overcome this limitation, researchers propose a novel framework called LiDAR-RT. This framework uses efficient and effective rendering pipelines that integrate Gaussian primitives and hardware-accelerated ray tracing technology. The framework can realistically render LiDAR views while allowing for flexible scene editing operations and various sensor configurations. The results show that LiDAR-RT outperforms state-of-the-art methods in terms of rendering quality and efficiency. |
