Multi-feature Radiance Baking Neural Networks for Instant Volumetric Rendering

Photorealistic volume rendering suffers from slow convergence with traditional path tracing algorithms and poses significant challenges for real-time applications. In this paper, we present Multi-feature Radiance Baking Neural Networks (MRBNN), a neural volumetric rendering method that achieves real-time performance by leveraging analytic decomposition of scattering with an efficient learned representation. Our method reformulates in-scattering integral in Volumetric Radiance Transfer Equation to diagonal scaling operator in spherical harmonics spectrum. Building on this formulation, we propose a compact factorized representation with low-rank compression in latent space that replaces costly integration. We then introduce efficient dual-pattern feature sampling and a lightweight neural decoder for fast radiance prediction. With these dedicated designs, MRBNN achieves 4 ∼ 5 × speedup over the state-of-the-art while maintaining high visual fidelity. Moreover, our method addresses key limitations of prior work, including over-illumination artifacts and inability to handle volumes with spatially varying albedo or heterogeneous phase parameters. Extensive experiments demonstrate that our neural baking method can synthesize photorealistic images for complex volumes in a few milliseconds.