3D Gaussian Splatting (3DGS) is a promising technique for 3D reconstruction, offering efficient training and rendering speeds, making it suitable for real-time applications.However, current methods require highly controlled environments (no moving people or wind-blown elements, and consistent lighting) to meet the inter-view consistency assumption of 3DGS. This makes reconstruction of real-world captures problematic. We present SpotlessSplats, an approach that leverages pre-trained and general-purpose features coupled with robust optimization to effectively ignore transient distractors. Our method achieves state-of-the-art reconstruction quality both visually and quantitatively, on casual captures.
3D高斯喷溅（3DGS）是一种有前景的三维重建技术，具备高效的训练和渲染速度，适用于实时应用。然而，当前的方法要求环境高度控制（无移动人物或被风吹动的元素，以及一致的照明），以满足3D高斯喷溅的视角一致性假设。这使得在现实世界捕捉的重建变得困难。我们提出了SpotlessSplats，一种利用预训练和通用特征结合强健优化来有效忽略瞬态干扰的方法。我们的方法在非正式捕捉条件下，在视觉和定量上实现了最先进的重建质量。