  Implicit neural representation methods have shown impressive advancements in
learning 3D scenes from unstructured in-the-wild photo collections but are
still limited by the large computational cost of volumetric rendering. More
recently, 3D Gaussian Splatting emerged as a much faster alternative with
superior rendering quality and training efficiency, especially for small-scale
and object-centric scenarios. Nevertheless, this technique suffers from poor
performance on unstructured in-the-wild data. To tackle this, we extend over 3D
Gaussian Splatting to handle unstructured image collections. We achieve this by
modeling appearance to seize photometric variations in the rendered images.
Additionally, we introduce a new mechanism to train transient Gaussians to
handle the presence of scene occluders in an unsupervised manner. Experiments
on diverse photo collection scenes and multi-pass acquisition of outdoor
landmarks show the effectiveness of our method over prior works achieving
state-of-the-art results with improved efficiency.
