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.
尽管3D场景重建领域由于其逼真的质量而主要由NeRFs（神经辐射场）主导，但最近3D高斯喷溅（3DGS）技术已经出现，提供了类似的质量并具备实时渲染速度。然而，这两种方法主要在受控的3D场景中表现出色，而在自然环境中的数据——特点是遮挡、动态对象和变化的光照——依然具有挑战性。NeRFs能通过每张图片的嵌入向量轻松适应这种条件，但由于3DGS的显式表示和缺乏共享参数，它在处理这些问题上遇到困难。为了解决这一问题，我们引入了一种名为WildGaussians的新方法，该方法通过利用强大的DINO特征并在3DGS中整合外观建模模块，有效处理遮挡和外观变化。我们证明了WildGaussians在保持3DGS的实时渲染速度的同时，在处理自然环境数据方面超越了3DGS和NeRF的基线，且这一切都在一个简单的架构框架内实现。