标题： 基于物理的神经 LiDAR 重模拟 显示英文标题

标题： Physically Based Neural LiDAR Resimulation

摘要： 用于新视角合成（NVS）的方法最近在LiDAR模拟和大规模3D场景重建领域引起了关注。 虽然已经提出了更快渲染或处理动态场景的解决方案，但LiDAR特有的效果仍未得到充分解决。 通过显式建模传感器特性，如滚动快门、激光功率变化和强度衰减，我们的方法相比现有技术实现了更精确的LiDAR模拟。 我们通过与最先进方法的定量和定性比较，以及突出每个传感器模型组件重要性的消融研究，证明了我们方法的有效性。 除此之外，我们展示了我们的方法具有先进的重模拟能力，例如生成相机视角下的高分辨率LiDAR扫描。 我们的代码和生成的数据集可在 https://github.com/richardmarcus/PBNLiDAR 获取。 显示英文摘要

摘要： 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.