GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912475043069952 |
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| author | Ye, Kai Gao, Chong Li, Guanbin Chen, Wenzheng Chen, Baoquan |
| author_facet | Ye, Kai Gao, Chong Li, Guanbin Chen, Wenzheng Chen, Baoquan |
| contents | Recent 3D Gaussian Splatting (3DGS) representations have demonstrated remarkable performance in novel view synthesis; further, material-lighting disentanglement on 3DGS warrants relighting capabilities and its adaptability to broader applications. While the general approach to the latter operation lies in integrating differentiable physically-based rendering (PBR) techniques to jointly recover BRDF materials and environment lighting, achieving a precise disentanglement remains an inherently difficult task due to the challenge of accurately modeling light transport. Existing approaches typically approximate Gaussian points' normals, which constitute an implicit geometric constraint. However, they usually suffer from inaccuracies in normal estimation that subsequently degrade light transport, resulting in noisy material decomposition and flawed relighting results. To address this, we propose GeoSplatting, a novel approach that augments 3DGS with explicit geometry guidance for precise light transport modeling. By differentiably constructing a surface-grounded 3DGS from an optimizable mesh, our approach leverages well-defined mesh normals and the opaque mesh surface, and additionally facilitates the use of mesh-based ray tracing techniques for efficient, occlusion-aware light transport calculations. This enhancement ensures precise material decomposition while preserving the efficiency and high-quality rendering capabilities of 3DGS. Comprehensive evaluations across diverse datasets demonstrate the effectiveness of GeoSplatting, highlighting its superior efficiency and state-of-the-art inverse rendering performance. The project page can be found at https://pku-vcl-geometry.github.io/GeoSplatting/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_24204 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering Ye, Kai Gao, Chong Li, Guanbin Chen, Wenzheng Chen, Baoquan Computer Vision and Pattern Recognition Recent 3D Gaussian Splatting (3DGS) representations have demonstrated remarkable performance in novel view synthesis; further, material-lighting disentanglement on 3DGS warrants relighting capabilities and its adaptability to broader applications. While the general approach to the latter operation lies in integrating differentiable physically-based rendering (PBR) techniques to jointly recover BRDF materials and environment lighting, achieving a precise disentanglement remains an inherently difficult task due to the challenge of accurately modeling light transport. Existing approaches typically approximate Gaussian points' normals, which constitute an implicit geometric constraint. However, they usually suffer from inaccuracies in normal estimation that subsequently degrade light transport, resulting in noisy material decomposition and flawed relighting results. To address this, we propose GeoSplatting, a novel approach that augments 3DGS with explicit geometry guidance for precise light transport modeling. By differentiably constructing a surface-grounded 3DGS from an optimizable mesh, our approach leverages well-defined mesh normals and the opaque mesh surface, and additionally facilitates the use of mesh-based ray tracing techniques for efficient, occlusion-aware light transport calculations. This enhancement ensures precise material decomposition while preserving the efficiency and high-quality rendering capabilities of 3DGS. Comprehensive evaluations across diverse datasets demonstrate the effectiveness of GeoSplatting, highlighting its superior efficiency and state-of-the-art inverse rendering performance. The project page can be found at https://pku-vcl-geometry.github.io/GeoSplatting/. |
| title | GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.24204 |