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.

翻译：尽管神经辐射场（NeRF）因其逼真的画质主导了3D场景重建领域，但3D高斯溅射（3DGS）近期崭露头角，在提供同等质量的同时实现了实时渲染速度。然而，这两种方法主要在受控良好的3D场景中表现优异，而对于具有遮挡、动态物体和变化光照等特征的野外数据，仍然存在挑战。NeRF可以通过每张图像的嵌入向量轻松适应此类条件，但3DGS由于其显式表示和缺乏共享参数而难以应对。为此，我们提出了WildGaussians，一种利用3DGS处理遮挡和外观变化的新方法。通过利用鲁棒的DINO特征并在3DGS中集成外观建模模块，我们的方法取得了最先进的结果。我们证明，WildGaussians在保持3DGS实时渲染速度的同时，在野外数据处理方面超越了3DGS和NeRF基线，且所有功能均在一个简洁的架构框架内实现。