VeilSplat: Separating Changing Smoke from 3D Gaussian Scenes

Smoke-free novel-view synthesis seeks to recover a clear, consistent scene from images captured through changing smoke. Existing approaches often rely on pretrained depth models or generative restoration priors. We introduce Veil- Splat, a physics-based Gaussian reconstruction method that jointly recovers scene and medium from posed smoky RGB images without external pretrained net- works. Our key principle is to separate surface coverage from medium trans- mission: smoke attenuates and redistributes radiance while the direct rendering branch retains the scene’s occlusion weights. A compact density field combines shared spatial bases with per-view coefficients to capture spatially nonuniform smoke variation. Path-sampled optical thickness couples this field to attenu- ated direct radiance, broadened forward scattering, and airlight. The recovered scene supports clean novel views through ordinary Gaussian splatting. On five RealX3D smoke scenes, VeilSplat leads all five evaluated baselines with mean PSNR/SSIM/LPIPS of 16.30/0.6288/0.5083, gaining 5.44 dB PSNR over the strongest baseline. Matched component studies establish the benefits of cover- age separation, capture-varying transmission and forward scattering.