Radiometrically Consistent Gaussian Surfels for Inverse Rendering

TL;DR

Radiometric consistency is introduced, a novel physically-based constraint that provides supervision towards unobserved views by minimizing the residual between each Gaussian primitive's learned radiance and its physically-based rendered counterpart, and RadioGS, an inverse rendering framework built upon this principle.

Abstract

Inverse rendering with Gaussian Splatting has advanced rapidly, but accurately disentangling material properties from complex global illumination effects, particularly indirect illumination, remains a major challenge. Existing methods often query indirect radiance from Gaussian primitives pre-trained for novel-view synthesis. However, these pre-trained Gaussian primitives are supervised only towards limited training viewpoints, thus lack supervision for modeling indirect radiances from unobserved views. To address this issue, we introduce radiometric consistency, a novel physically-based constraint that provides supervision towards unobserved views by minimizing the residual between each Gaussian primitive's learned radiance and its physically-based rendered counterpart. Minimizing the residual for unobserved views establishes a self-correcting feedback loop that provides supervision from both physically-based rendering and novel-view synthesis, enabling accurate modeling of inter-reflection. We then propose Radiometrically Consistent Gaussian Surfels (RadioGS), an inverse rendering framework built upon our principle by efficiently integrating radiometric consistency by utilizing Gaussian surfels and 2D Gaussian ray tracing. We further propose a finetuning-based relighting strategy that adapts Gaussian surfel radiances to new illuminations within minutes, achieving low rendering cost (<10ms). Extensive experiments on existing inverse rendering benchmarks show that RadioGS outperforms existing Gaussian-based methods in inverse rendering, while retaining the computational efficiency.

Figures (20)

• Figure 1: We introduce RadioGS, a novel inverse rendering framework that models accurate indirect illumination by providing a novel physically-based supervision on unobserved directions. (a) Compared to existing Gaussian-based methods gu2024irgssun2025svgir, our method provides realistic inter-reflection between the red bulb and the blobs on the yellow lego surface, (b) leading to robust decomposition of scene properties. (c) Our method can also generate realistic indirect illumination on new lighting conditions for real objects from Stanford-ORB dataset kuang2023stanfordorb.
• Figure 2: Overview of our RadioGS.Left: Our radiometric consistency loss $\mathcal{L}_{rad}$ provides physically-based supervision on indirect radiances from views unobserved by image reconstruction loss $\mathcal{L}_{recon}$, by enforcing consistency between surfel radiance $L_\mathbf{G}$ and physically-based rendered (PBR) radiance $L_\mathbf{G}^\mathbf{PBR}$ of Gaussian surfels. Radiometric consistency is seamlessly integrated into the inverse rendering framework, guiding Gaussian surfels to obtain physically-based radiance for delivering realistic indirect radiance to other surfels. 2D Gaussian ray tracing is deployed to jointly optimize ray-traced Gaussian surfels with our radiometric consistency loss. Right: Black-dotted arrows show NVS supervision, which leaves the occluded green Gaussian unconstrained. Pink-dotted arrows show our radiometric consistency providing additional supervision on its outgoing radiance along unseen directions (e.g., towards other Gaussian surfels).
• Figure 3: Qualitative result on the "lego" scene of TensoIR dataset. Our method provides enhanced decomposition and realistic relighting results compared to Gaussian-based methods. Specifically, our method shows noticeably robust performance on regions with high geometric complexity, such as the highlighted bucket. Best viewed in zoom.
• Figure 4: Relighting results on the "armadillo" scene of TensoIR dataset.
• Figure 5: Qualitative results on the "hotdog" scene of Synthetic4Relight zhang2022invrender dataset. Our method models natural inter-reflection between the sausages and the buns, showing superior reconstruction performance on highlighted regions. IRGS shows relatively bright and fluctuating indirect illumination, which led to darker albedo reconstruction. SVG-IR models relatively darker indirect illumination, returning brighter albedo reconstruction. Best viewed in zoom.