prs-eth/PanoInfinigen
🗃️ PanoInfinigen Dataset PanoInfinigen 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, plus the iCity procedural city generator for the urban split. It serves as the primary training data for PaGeR, a single-step diffusion model for zero-shot panoramic depth… See the full description on the dataset page: https://huggingface.co/datasets/prs-eth/PanoInfinigen.
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🗂️ PanoInfinigen: Dữ liệu panoramic 3 trong 1 sinh từ công cụ thực hiện
Bộ dữ liệu ảnh panoramic đồng thời RGB, Depth, và Surface Normals, được sinh ngẫu nhiên từ Infinigen và iCity.
PanoInfinigen là một bộ dữ liệu ảnh panoramic đồng thời ba kênh RGB, Depth và Surface Normals, sinh ngẫu nhiên bằng công cụ Infinigen đã được sửa đổi hỗ trợ hình học góc rộng và bộ sinh thành phố thủ đô iCity. Dữ liệu được chia thành ba cấu hình: indoor, nature, urban, mỗi cấu hình có các split train, val, test. Đây là dữ liệu huấn luyện chính cho PaGeR, mô hình diffusion bước đơn ước lượng chiều sâu và bề mặt panoramic zero-shot.
✨ Điểm nổi bật
- RGB 8-bit PNG: Ảnh panoramic dựng hình ERP (Equirectangular Projection).
- Depth float16 .npy: Mảng nhị phân lưu trữ độ chính xác cao, phạm vi [0, 75] m (indoor/nature), [0, 500] m (urban).
- Normals float16 .npy: Mảng nhị phân lưu trữ hướng mặt phẳng bề mặt, giá trị [-1, 1].
- 3 configs + 3 splits: indoor, nature, urban, mỗi config có train, validation, test.
- Sinh ngẫu nhiên hoàn toàn: Dùng Infinigen (indoor, nature) và iCity (urban) tạo cảnh đồng nhất.
- Công cụ hỗ trợ: Parquet, Dask, Polars, mlcroissant qua Hugging Face Datasets.
- License phân biệt: indoor + nature dùng BSD-3-Clause; urban dùng CC BY-NC 4.0.
💡 Vì sao đáng chú ý
Khác với bộ dữ liệu thực tế chất lượng cao nhưng ít và khó gán nhãn, PanoInfinigen cung cấp hàng chục nghìn mẫu ảnh panoramic 360° với siêu nhãn chính xác tuyệt đối (depth và normals được đồng bộ pixel hoàn hảo). Điều này giúp huấn luyện PaGeR, mô hình diffusion bước đơn, hoạt động zero-shot mà không cần dữ liệu thực tế. Phù hợp cho nghiên cứu ước lượng chiều sâu và bề mặt trong môi trường VR/AR, robot, xe tự hành, bản đồ 3D.
#PanoramicDepth #SyntheticDataset #Infinigen #HuggingFace #TiniX
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