  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. Additional results available at:
https://spotlesssplats.github.io
