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

该论文旨在检测不同摄像机位置和不同时间实例获得的同一3D场景的两幅“自然环境中”的图像之间发生的变化，通过贡献一个基于合成数据训练的、类别不可知的变化检测模型，使用“登记与差异”方法，能够在没有精细调整的情况下对真实世界图像进行准确处理，并且无需额外的地面真实数据。同时，作者还提供了一组用于评估的实际图像对，其中包含人工标注的差异，并展示了该方法的有效性。

改变你所期望的(现在以3D呈现)