Oct 17, 2026J/K move · Enter open · S save
Yimeng Dou, Adam M. Saunders, Andre T. S. Hucke, Michael E. Kim+4
Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA · Department of Computer Science, Vanderbilt University, Nashville, TN, USA · Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA+4
Many real-world capture scenarios involve an active light source rigidly attached to the camera: camera flashes for dark-environment imaging, underwater vehicle lights, and endoscopic/laparoscopic medical procedures. As the camera moves, the co-mounted light moves with it, causing the illumination direction at each surface point to change across views. Neural rendering methods (NeRF, Gaussian Splatting) encode this pose-dependent illumination as surface coloration, producing appearance inconsistencies and making it harder to recover the underlying scene. We present DaFISH, which can be plugged into any novel-view synthesis (NVS) pipeline such as NeRF. It factorizes per-point emitted color into surface albedo and a spherical harmonic (SH)-represented illumination factor. During training, the SH illumination is evaluated directly, enabling stable joint optimization of albedo, lighting, and geometry. DaFISH provides analytical generalization of illumination to arbitrary camera poses without retraining. Building on the closed-form SH-to-irradiance conversion, the training pipeline is fully differentiable from SH evaluation through volume rendering. Averaged over the held-out frames of the five scenes, DaFISH attains the best mean PSNR (38.91,dB), SSIM (0.9613), and LPIPS (0.0582) among all compared methods. It improves over the strongest splatting baseline as well as the closest NeRF-based baseline, while recovering the scene consistently across views compared to prior methods.