ChamudithaJay/PASLCD

Multi-View Pose-Agnostic Change Localization with Zero Labels (CVPR - 2025)

We introduce the Pose-Agnostic Scene-Level Change Detection Dataset (PASLCD), comprising data collected from 10 complex, real-world scenes, including 5 indoor and 5 outdoor environments. PASLCD enables the evaluation of scene-level change detection, with multiple simultaneous changes per scene and ``distractor'' visual changes (i.e.~varying lighting, shadows, or reflections). Among the indoor and outdoor scenes, two are 360 scenes, while the remaining three are front-facing (FF) scenes.

For all 10 scenes in PASLCD, there are two available change detection instances: (1) change detection under consistent lighting conditions, and (2) change detection under varied lighting conditions. Images were captured using an iPhone following a random and independent trajectory for each scene instance. We provide 50 human-annotated change segmentation masks per scene, totaling 500 annotated masks for the dataset.

Please refer to the full paper for further information about the dataset and the proposed approach.

Citation

@inproceedings{galappaththige2025multi,
  title={Multi-View Pose-Agnostic Change Localization with Zero Labels},
  author={Galappaththige, Chamuditha Jayanga and Lai, Jason and Windrim, Lloyd and Dansereau, Donald and Sunderhauf, Niko and Miller, Dimity},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  pages={11600--11610},
  year={2025}
}