Physics-grounded Neural Radar Fields for High-fidelity Novel View Synthesis

Radar-based novel view synthesis (NVS) supports robust scene understanding and radar simulation for autonomous driving, particularly under poor illumination and adverse weather. However, most existing methods simplify radar measurement formation and primarily model scene responses, leaving radar-specific artifacts insufficiently represented. This limits the fidelity of synthesized data to real radar measurements. The resulting synthetic-to-real gap can hinder the use of synthesized data in downstream tasks. To narrow this gap, we propose PhyRF, a physics-grounded neural radar field framework for high-fidelity radar-based NVS. For points sampled along each radar beam, the neural radar field predicts scene occupancy, radar reflectivity, and artifact contribution. We propose a differentiable renderer grounded in radar measurement formation that combines these predictions according to their distinct physical roles to synthesize measurements. To help disentangle scene geometry from artifacts during training, we further introduce LiDAR measurements as additional supervision by optimizing a shared geometry field. Extensive experiments demonstrate that PhyRF synthesizes radar measurements with higher fidelity to real observations than existing methods.