update README.md

README.md

@@ -91,6 +91,52 @@ semantic = sample["semantic"]
 instance = sample["instance"]
 ```
 
+### 🔄 Recover Original Values from TIFF Images
+
+The dataset uses .tiff format for all dense outputs to preserve precision and visual compatibility. Here’s how to revert them back to their original values:
+
+```python
+from datasets import load_dataset
+from huggingface_hub import snapshot_download
+from PIL import Image
+import numpy as np
+import json
+import os
+
+# Load sample
+dataset = load_dataset("jagennath-hari/nyuv2", split="train")
+sample = dataset[0]
+
+# Download and load scaling metadata
+local_dir = snapshot_download(
+    repo_id="jagennath-hari/nyuv2",
+    repo_type="dataset",
+    allow_patterns="scaling_factors.json"
+)
+with open(os.path.join(local_dir, "scaling_factors.json")) as f:
+    scale = json.load(f)
+
+depth_scale = scale["depth_scale"]           # e.g., 1000.0
+label_max = scale["label_max_value"]         # e.g., 894
+instance_max = scale["instance_max_value"]   # e.g., 37
+
+# === Unscale depth (mm → m)
+depth_img = np.array(sample["depth"])
+depth_m = depth_img.astype(np.float32) / depth_scale
+
+# === Unscale semantic mask
+sem_scaled = np.array(sample["semantic"])
+semantic_labels = np.round(
+    sem_scaled.astype(np.float32) * (label_max / 65535.0)
+).astype(np.uint16)
+
+# === Unscale instance mask
+inst_scaled = np.array(sample["instance"])
+instance_ids = np.round(
+    inst_scaled.astype(np.float32) * (instance_max / 65535.0)
+).astype(np.uint16)
+```
+
 ## 📄 Citation
 
 If you use this dataset, please cite the original authors: