README.md

---
license: mit
tags:
- Change_Detection
- Pose_Agnostic_Change_Detection
- Scene_Change_Detection
- Multi-View_Change_Detection
pretty_name: Pose-Agnostic Scene Level Change Detection
size_categories:
- 1K<n<10K
---


# 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](https://chumsy0725.github.io/MV-3DCD/) for further information about the dataset and the proposed approach. 

## Citation
```shell

@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}
}
```