  The goal of this paper is to detect what has changed, if anything, between
two "in the wild" images of the same 3D scene acquired from different camera
positions and at different temporal instances. The open-set nature of this
problem, occlusions/dis-occlusions due to the shift in viewpoint, and the lack
of suitable training datasets, presents substantial challenges in devising a
solution.
  To address this problem, we contribute a change detection model that is
trained entirely on synthetic data and is class-agnostic, yet it is performant
out-of-the-box on real world images without requiring fine-tuning. Our solution
entails a "register and difference" approach that leverages self-supervised
frozen embeddings and feature differences, which allows the model to generalise
to a wide variety of scenes and domains. The model is able to operate directly
on two RGB images, without requiring access to ground truth camera intrinsics,
extrinsics, depth maps, point clouds, or additional before-after images.
Finally, we collect and release a new evaluation dataset consisting of
real-world image pairs with human-annotated differences and demonstrate the
efficacy of our method. The code, datasets and pre-trained model can be found
at: https://github.com/ragavsachdeva/CYWS-3D
