Neural Inverse Rendering from Propagating Light
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Abstract (arXiv)
We present the first system for physically based, neural inverse rendering from multi-viewpoint videos of propagating light. Our approach relies on a time-resolved extension of neural radiance caching -- a technique that accelerates inverse rendering by storing infinite-bounce radiance arriving at any point from any direction. The resulting model accurately accounts for direct and indirect light transport effects and, when applied to captured measurements from a flash lidar system, enables state-of-the-art 3D reconstruction in the presence of strong indirect light. Further, we demonstrate view synthesis of propagating light, automatic decomposition of captured measurements into direct and indirect components, as well as novel capabilities such as multi-view time-resolved relighting of captured scenes.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 42 of its 95 references that are in the map. First posted on arXiv 5 Jun 2025.
Builds on
42 of 95 references.
References · 42
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- 2023TensoIR: Tensorial Inverse Renderingcited by 168
30 more
- 2023NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Imagescited by 163
- 20243D Gaussian Ray Tracing: Fast Tracing of Particle Scenescited by 139
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