GS-ROR: 3D Gaussian Splatting for Reflective Object Relighting via SDF Priors

• URL: http://arxiv.org/abs/2406.18544v2
• Date: Thu, 12 Sep 2024 13:28:51 GMT
• Title: GS-ROR: 3D Gaussian Splatting for Reflective Object Relighting via SDF Priors
• Authors: Zuo-Liang Zhu, Beibei Wang, Jian Yang,
• Abstract summary: We propose GS-ROR for reflective objects relighting with 3DGS aided by SDF priors. Our method outperforms the existing Gaussian-based inverse rendering methods in terms of relighting quality.
• Score: 20.523085632567717
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 3D Gaussian Splatting (3DGS) has shown a powerful capability for novel view synthesis due to its detailed expressive ability and highly efficient rendering speed. Unfortunately, creating relightable 3D assets with 3DGS is still problematic, particularly for reflective objects, as its discontinuous representation raises difficulties in constraining geometries. Inspired by previous works, the signed distance field (SDF) can serve as an effective way for geometry regularization. However, a direct incorporation between Gaussians and SDF significantly slows training. To this end, we propose GS-ROR for reflective objects relighting with 3DGS aided by SDF priors. At the core of our method is the mutual supervision of the depth and normal between deferred Gaussians and SDF, which avoids the expensive volume rendering of SDF. Thanks to this mutual supervision, the learned deferred Gaussians are well-constrained with a minimal time cost. As the Gaussians are rendered in a deferred shading mode, while the alpha-blended Gaussians are smooth, individual Gaussians may still be outliers, yielding floater artifacts. Therefore, we further introduce an SDF-aware pruning strategy to remove Gaussian outliers, which are located distant from the surface defined by SDF, avoiding the floater issue. Consequently, our method outperforms the existing Gaussian-based inverse rendering methods in terms of relighting quality. Our method also exhibits competitive relighting quality compared to NeRF-based methods with at most 25% of training time and allows rendering at 200+ frames per second on an RTX4090.

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