模拟激光雷达真实传感器效应，提升3D场景重建精度。
Physically Based Neural LiDAR Resimulation
- 显式建模滚动快门、激光功率变化等传感器特性。
- 相比现有方法，点云密度与强度分布更接近真实数据。
- 适合需要高保真激光雷达仿真的自动驾驶研究者。
基于新型视图合成（NVS）的方法在激光雷达仿真与大规模3D场景重建中日益受到关注。尽管已有加速渲染或动态场景处理方案，但激光雷达特有的传感器效应仍未充分解决。本文通过显式建模滚动快门、激光功率波动及强度衰减等传感器特性，实现了比现有技术更精确的激光雷达仿真。我们在定量和定性对比中验证了该方法的有效性，并通过消融实验凸显各组件的重要性。此外，本方法展现出先进的重仿真能力，例如可生成相机视角下的高分辨率激光雷达扫描。代码与数据集已公开于 https://github.com/richardmarcus/PBNLiDAR。
原文摘要 · Abstract (English)
Methods for Novel View Synthesis (NVS) have recently found traction in the field of LiDAR simulation and large-scale 3D scene reconstruction. While solutions for faster rendering or handling dynamic scenes have been proposed, LiDAR specific effects remain insufficiently addressed. By explicitly modeling sensor characteristics such as rolling shutter, laser power variations, and intensity falloff, our method achieves more accurate LiDAR simulation compared to existing techniques. We demonstrate the effectiveness of our approach through quantitative and qualitative comparisons with state-of-the-art methods, as well as ablation studies that highlight the importance of each sensor model component. Beyond that, we show that our approach exhibits advanced resimulation capabilities, such as generating high resolution LiDAR scans in the camera perspective. Our code and the resulting dataset are available at https://github.com/richardmarcus/PBNLiDAR.
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