GOGS: High-Fidelity Geometry and Relighting for Glossy Objects via Gaussian Surfels
This work addresses the challenge of high-fidelity geometry and relighting for glossy objects in computer vision, which is important for applications like virtual reality and digital content creation, but it appears incremental as it builds on existing methods like 3D Gaussian Splatting.
The paper tackles the problem of inverse rendering for glossy objects from RGB imagery, which suffers from ambiguity and computational inefficiency, by proposing GOGS, a two-stage framework using 2D Gaussian surfels that achieves state-of-the-art performance in geometry reconstruction, material separation, and photorealistic relighting.
Inverse rendering of glossy objects from RGB imagery remains fundamentally limited by inherent ambiguity. Although NeRF-based methods achieve high-fidelity reconstruction via dense-ray sampling, their computational cost is prohibitive. Recent 3D Gaussian Splatting achieves high reconstruction efficiency but exhibits limitations under specular reflections. Multi-view inconsistencies introduce high-frequency surface noise and structural artifacts, while simplified rendering equations obscure material properties, leading to implausible relighting results. To address these issues, we propose GOGS, a novel two-stage framework based on 2D Gaussian surfels. First, we establish robust surface reconstruction through physics-based rendering with split-sum approximation, enhanced by geometric priors from foundation models. Second, we perform material decomposition by leveraging Monte Carlo importance sampling of the full rendering equation, modeling indirect illumination via differentiable 2D Gaussian ray tracing and refining high-frequency specular details through spherical mipmap-based directional encoding that captures anisotropic highlights. Extensive experiments demonstrate state-of-the-art performance in geometry reconstruction, material separation, and photorealistic relighting under novel illuminations, outperforming existing inverse rendering approaches.