Add urban config; rename urban shards to uniform naming

Register `urban` as a BuilderConfig in README; rename urban parquet shards from `{split}-AAAAA-BBBBB.parquet` to `{split}-NNNNN.parquet` for parity with indoor / nature.

README.md

@@ -17,6 +17,14 @@ configs:
     path: "data/nature/val-*"
   - split: test
     path: "data/nature/test-*"
+- config_name: urban
+  data_files:
+  - split: train
+    path: "data/urban/train-*"
+  - split: val
+    path: "data/urban/val-*"
+  - split: test
+    path: "data/urban/test-*"
 dataset_info:
 - config_name: indoor
   features:
@@ -34,6 +42,14 @@ dataset_info:
     dtype: binary
   - name: normals
     dtype: binary
+- config_name: urban
+  features:
+  - name: image
+    dtype: image
+  - name: depth
+    dtype: binary
+  - name: normals
+    dtype: binary
 ---
 
 # 🗃️ Pano-Infinigen Dataset
@@ -62,18 +78,18 @@ dataset_info:
 It serves as the primary training data for [PaGeR](https://pager360.github.io/), a single-step diffusion model for zero-shot panoramic depth and normal estimation.
 
 ## Dataset Summary
-- **Content:** Synthetic indoor and outdoor(nature) scenes.
+- **Content:** Synthetic indoor, nature, and urban scenes.
 - **Modality:** RGB (PNG), Depth (binary .npy), Surface Normals (binary .npy).
 - **Projection:** Equirectangular (ERP).
 - **Use Case:** Training and evaluating monocular panoramic depth and normal estimation models.
 
 ## Data Structure
-The dataset is split into two configurations: `indoor` and `nature`. Each contains `train`, `validation`, and `test` splits.
+The dataset is split into three configurations: `indoor`, `nature`, and `urban`. Each contains `train`, `validation`, and `test` splits.
 
 | Feature | Type | Description |
 | :--- | :--- | :--- |
 | `image` | `PIL.Image` | 8-bit RGB Panoramic Image. |
-| `depth` | `binary` | **float16** NumPy array. Range: [0, 75] meters. |
+| `depth` | `binary` | **float16** NumPy array. Range: [0, 75] m for `indoor` / `nature`, [0, 500] m for `urban`. |
 | `normals` | `binary` | **float16** NumPy array. Range: [-1, 1]. |
 
 
@@ -86,7 +102,7 @@ import io
 import numpy as np
 from datasets import load_dataset
 
-# Load the indoor training split
+# Load the indoor training split (swap name for "nature" or "urban")
 ds = load_dataset("prs-eth/PanoInfinigen", name="indoor", split="train")
 
 sample = ds[0]
@@ -94,7 +110,8 @@ sample = ds[0]
 # 1. Get RGB Image
 rgb = sample["image"]
 
-# 2. Convert Binary Depth to NumPy (float16, 0-75m)
+# 2. Convert Binary Depth to NumPy (float16)
+#    Max range: 75 m (indoor / nature), 500 m (urban)
 depth = np.load(io.BytesIO(sample["depth"]))
 
 # 3. Convert Binary Normals to NumPy (float16, -1 to 1)