🗃️ Pano-Infinigen Dataset

Pano-Infinigen is a synthetic dataset of high-resolution panoramic images in ERP, featuring perfectly aligned RGB, Depth, and Surface Normals. This dataset was generated using a modified Infinigen framework to support wide-angle panoramic geometry.

It serves as the primary training data for PaGeR, a single-step diffusion model for zero-shot panoramic depth and normal estimation.

Dataset Summary

• Content: Synthetic indoor and outdoor 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 outdoor. 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.
normals	binary	float16 NumPy array. Range: [-1, 1].

How to Use

Since depth and normals are stored as binary blobs to preserve precision (float16), you need to use io.BytesIO to load them back into NumPy.

import io
import numpy as np
from datasets import load_dataset

# Load the indoor training split
ds = load_dataset("prs-eth/Pano-Infinigen", name="indoor", split="train")

sample = ds[0]

# 1. Get RGB Image
rgb = sample["image"]

# 2. Convert Binary Depth to NumPy (float16, 0-75m)
depth = np.load(io.BytesIO(sample["depth"]))

# 3. Convert Binary Normals to NumPy (float16, -1 to 1)
normals = np.load(io.BytesIO(sample["normals"]))