acceptodds
Under review as a conference paper at ICLR 2027

Efficient Forward Path Tracing on Planar-Based Gaussian Splatting for Inverse Rendering

Abstract

Gaussian splatting has emerged as a promising representation for novel view synthesis, inverse rendering, and relighting, owing to its efficient rasterization and high-fidelity scene representation. Existing Gaussian-based inverse rendering methods typically adopt forward or deferred shading with precomputed or approximate visibility and indirect illumination, limiting their ability to model complex global light transport. Gaussian ray tracing methods can explicitly model visibility and indirect illumination, but incur high computational cost due slow ray-Gaussian intersection. Therefore, accurately disentangling material properties from illumination under complex global illumination effects while maintaining computational efficiency remains a key challenge in Gaussian-based inverse rendering. To address this challenge, we propose an efficient inverse rendering framework that combines planar-based Gaussians with the reconstructed scene mesh for efficient and physically-based material and illumination optimization. In the geometry reconstruction stage, we adopt the modified VA-GS to reconstruct accurate scene geometry, which is then kept fixed during inverse rendering. Based on the reconstructed geometry, we introduce a per-Gaussian forward shading method that leverages the scene mesh to query visibility and model multi-bounce light transport, enabling the outgoing radiance of each Gaussian primitive to be computed before rasterization. The resulting per-Gaussian radiance is -blended into the rendered image through differentiable Gaussian splatting to jointly optimize the material and illumination parameters. Experiments demonstrate that our method outperforms existing inverse rendering baselines while maintaining high-fidelity novel view synthesis.

open until 14 Dec 2026

est. 32% chance this paper gets accepted at ICLR 2027.

Reject 68%Accept 32%

What do you think this paper will get?

All positions stay anonymous.

Related papers

Loading the map…

Discussion (0)

Sign in to comment.