update README.md

README.md

@@ -46,13 +46,31 @@ configs:
 
 # NYUv2
 
+NYUv2 is a benchmark RGB-D dataset widely used for scene understanding tasks such as:
 
-## Dataset Description
+- Indoor semantic segmentation
+- Depth estimation
+- Instance segmentation
 
 - **Homepage:** [NYU Depth Dataset V2](https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html)
 - **Paper:** [Indoor Segmentation and Support Inference from RGBD Images](https://cs.nyu.edu/~fergus/datasets/indoor_seg_support.pdf)
 
-## Citation
+This version has been preprocessed to include aligned:
+- RGB images (`.png`)
+- Depth maps in millimeters (`.tiff`, `uint16`)
+- Semantic masks (`.tiff`, scaled `uint16`)
+- Instance masks (`.tiff`, scaled `uint16`)
+
+Each sample is annotated with a consistent `id` and split across train/val/test.
+
+## 🧾 Dataset Metadata
+
+Additional files included:
+- `camera_params.json` — camera intrinsics and distortion
+- `class_names.json` — mapping from class IDs to human-readable names
+- `scaling_factors.json` — used for label/mask de-scaling during training
+
+## 📄 Citation
 
 If you use this dataset, please cite the original authors:
 
@@ -62,5 +80,4 @@ If you use this dataset, please cite the original authors:
   title     = {Indoor Segmentation and Support Inference from RGBD Images},
   booktitle = {ECCV},
   year      = {2012}
-}
-```
+}