High-Fidelity Dynamic LiDAR Re-simulation using Compositional Neural Fields

DyNFL, a novel neural field-based approach, enables high-fidelity re-simulation of LiDAR scans in dynamic driving scenes by constructing an editable neural field representation that integrates reconstructed static background and dynamic objects.