WildGaussians: 3D Gaussian Splatting in the Wild

• URL: http://arxiv.org/abs/2407.08447v2
• Date: Thu, 31 Oct 2024 12:58:08 GMT
• Title: WildGaussians: 3D Gaussian Splatting in the Wild
• Authors: Jonas Kulhanek, Songyou Peng, Zuzana Kukelova, Marc Pollefeys, Torsten Sattler,
• Abstract summary: We introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data.
• Score: 80.5209105383932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds. However, both methods primarily excel with well-controlled 3D scenes, while in-the-wild data - characterized by occlusions, dynamic objects, and varying illumination - remains challenging. NeRFs can adapt to such conditions easily through per-image embedding vectors, but 3DGS struggles due to its explicit representation and lack of shared parameters. To address this, we introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. By leveraging robust DINO features and integrating an appearance modeling module within 3DGS, our method achieves state-of-the-art results. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data, all within a simple architectural framework.

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