The Change You Want to See (Now in 3D)
Ragav Sachdeva, Andrew Zisserman

TL;DR
This paper introduces a synthetic-data-trained, class-agnostic change detection model for 3D scenes that generalizes well to real-world images without fine-tuning, using a register-and-difference approach with self-supervised embeddings.
Contribution
It presents a novel change detection method that operates on RGB images without needing camera parameters or additional data, trained solely on synthetic data for real-world applicability.
Findings
Effective on real-world images without fine-tuning
Operates directly on RGB images, no extra data needed
Outperforms existing methods on a new real-world dataset
Abstract
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…
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Taxonomy
TopicsAdvanced Vision and Imaging · Image Processing Techniques and Applications · Advanced Image and Video Retrieval Techniques
