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 backgrounds 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 \ShortName substantial improves dynamic scene
simulation based on LiDAR scans, offering a combination of physical fidelity
and flexible editing capabilities.

通过处理动态环境中的 LiDAR 测量和移动物体的边界框，我们介绍了一种名为 DyNFL 的新颖神经场方法，用于高保真度地重新模拟动态驾驶场景。我们的方法通过神经场构建、静态背景和动态物体的分离重建，允许用户在重新模拟的场景中修改视点、调整物体位置，并无缝地添加或移除物体。我们的方法的一个关键创新是神经场合成技术，通过光线投射测试有效地整合来自不同场景的重建神经资源，考虑到遮挡和透明表面。我们在合成和真实环境中的评估表明，DyNFL 显著改进了基于 LiDAR 扫描的动态场景模拟，提供了物理保真度和灵活的编辑能力的组合。