SceneFoam: Sparse-View Reconstruction of Native Radiance Foams

SceneFoam learns to reconstruct native Power Foam fields from sparse calibrated images, recovering bounded cells, internal surfaces, local texture, and view-dependent color. Its central design aligns appearance updates with the geometry that determines visibility and pixel-to-texture contributions. An image-conditioned frontend and native initializer are trained jointly to predict the scene. Four source-conditioned color-fitting steps then align directional radiance with the input photographs and independent reference images. A geometry-first refiner updates the surfaces, recomputes their image residuals and source-loss gradients, and predicts appearance using this refreshed evidence. Together, these stages couple a learned scene prior with observations evaluated on the current foam. On eight-view DL3DV140, SceneFoam obtains 27.97dB PSNR, 0.873 SSIM, and 0.153 LPIPS, outperforming ReSplat on all three metrics. On four native-PF comparison scenes, it improves all three metrics over both 30,000-update optimization baselines with complete inference in 8.94–9.44s. The recovered field directly supports rasterization, ray tracing, distorted cameras, and interaction through its tetrahedral dual. SceneFoam brings high-quality sparse-view acquisition to textured spatial partitions through geometry-consistent evidence updates.