Update README.md

README.md

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 pretty_name: Pose-Agnostic Scene Level Change Detection
 size_categories:
 - 1K<n<10K
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+---
+
+
+# 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 and the proposed method. 
+
+## Citation
+```shell
+@inproceedings{galappaththige2024mv3dcd,
+      title={Multi-View Pose-Agnostic Change Localization with Zero Labels}, 
+      author={Chamuditha Jayanga Galappaththige and Jason Lai and Lloyd Windrim and Donald Dansereau and Niko Suenderhauf and Dimity Miller},
+      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+      year={2025},
+}
+```