@inproceedings{
kulhanek2024wildgaussians,
title={WildGaussians: 3D Gaussian Splatting In the Wild},
author={Jonas Kulhanek and Songyou Peng and Zuzana Kukelova and Marc Pollefeys and Torsten Sattler},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NU3tE3lIqf}
}
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.
WildGaussians: 3D Gaussian Splatting In the Wild
Jonas Kulhanek Songyou Peng Zuzana Kukelova Marc Pollefeys Torsten Sattler