We introduce DyNFL, a novel neural field-based approach for high-fidelity re-simulation of LiDAR scans in dynamic driving scenes. DyNFL processes LiDAR measurements from dynamic environments, accompanied by bounding boxes of moving objects, to construct an editable neural field. This field, comprising separately reconstructed static background and dynamic objects, allows users to modify viewpoints, adjust object positions, and seamlessly add or remove objects in the re-simulated scene. A key innovation of our method is the neural field composition technique, which effectively integrates reconstructed neural assets from various scenes through a ray drop test, accounting for occlusions and transparent surfaces. Our evaluation with both synthetic and real-world environments demonstrates that DyNFL substantially improves dynamic scene LiDAR simulation, offering a combination of physical fidelity and flexible editing capabilities.

翻译：我们提出DyNFL，一种基于神经场的新方法，用于在动态驾驶场景中对激光雷达扫描进行高保真再模拟。DyNFL处理来自动态环境的激光雷达测量数据，并辅以运动物体的边界框，构建一个可编辑的神经场。该神经场由分别重建的静态背景和动态物体组成，允许用户在再模拟场景中修改视角、调整物体位置，并无缝添加或移除物体。该方法的关键创新在于神经场组合技术，该技术通过射线丢弃测试，有效整合来自不同场景的重建神经资产，并考虑遮挡和透明表面。我们在合成环境和真实环境中的评估表明，DyNFL显著改善了动态场景的激光雷达模拟，兼具物理保真度和灵活的编辑能力。