  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.
