Duplicate from ToferFish/RealX3D

Co-authored-by: Shuhong Liu <ToferFish@users.noreply.huggingface.co>

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README.md

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+---
+license: mit
+task_categories:
+  - image-to-3d
+  - depth-estimation
+  - image-to-image
+tags:
+  - 3d-reconstruction
+  - multi-view
+  - nerf
+  - 3d-gaussian-splatting
+  - novel-view-synthesis
+  - benchmark
+  - colmap
+  - point-cloud
+  - depth-map
+  - raw-image
+  - computational-photography
+pretty_name: "RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction"
+size_categories:
+  - 1K<n<10K
+---
+
+<div align="center">
+
+# RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction
+
+[![Project Page](https://img.shields.io/badge/🌐_Project_Page-RealX3D-blue?style=for-the-badge)](https://i2wm.github.io/3DRR_2026/)
+[![GitHub](https://img.shields.io/badge/GitHub-Code-black?style=for-the-badge&logo=github)](https://github.com/ShuhongLL/RealX3D)
+[![arXiv](https://img.shields.io/badge/arXiv-2512.23437-b31b1b?style=for-the-badge)](https://arxiv.org/abs/2512.23437)
+[![Challenge](https://img.shields.io/badge/🏆_3DRR_Challenge-NTIRE_@_CVPR_2026-purple?style=for-the-badge)](https://www.codabench.org/competitions/13854/)
+[![License](https://img.shields.io/badge/License-MIT-green?style=for-the-badge)](https://opensource.org/licenses/MIT)
+
+</div>
+
+**RealX3D** is a real-world benchmark dataset for multi-view 3D reconstruction under challenging capture conditions. It provides multi-view RGB images (both processed JPEG and Sony RAW), COLMAP sparse reconstructions, and high-precision 3D ground-truth geometry (point clouds, meshes, and rendered depth maps) across a diverse set of scenes and degradation types.
+
+<div align="center">
+<table>
+<tr>
+<td align="center"><b>🌙 Low Light</b></td>
+<td align="center"><b>💨 Smoke</b></td>
+</tr>
+<tr>
+<td align="center">
+<video src="https://raw.githubusercontent.com/I2WM/i2wm.github.io/main/3DRR_2026/static/videos/lowlight_teaser_compressed.mp4" width="400" controls autoplay muted loop></video>
+</td>
+<td align="center">
+<video src="https://raw.githubusercontent.com/I2WM/i2wm.github.io/main/3DRR_2026/static/videos/smoke_teaser_compressed.mp4" width="400" controls autoplay muted loop></video>
+</td>
+</tr>
+</table>
+</div>
+
+## ✨ Key Features
+
+- **9 real-world degradation conditions**: defocus (mild/strong), motion blur (mild/strong), low light, smoke, reflection, dynamic objects, and varying exposure.
+- **Full-resolution (\~7000×4700) and quarter-resolution (\~1800×1200)** JPEG images with COLMAP reconstructions.
+- **Sony RAW (ARW)** sensor data with complete EXIF metadata for 7 conditions.
+- **Per-frame metric depth maps** rendered from laser-scanned meshes.
+- **Camera poses and intrinsics** in both COLMAP binary format and NeRF-compatible `transforms.json`.
+
+## 📁 Dataset Structure
+
+```
+RealX3D/
+├── data/              # Full-resolution JPEG images + COLMAP reconstructions
+├── data_4/            # Quarter-resolution JPEG images + COLMAP reconstructions
+├── baseline_results/  # Baseline methods rendering results on data_4 for direct download
+├── data_arw/          # Sony RAW (ARW) sensor data
+├── pointclouds/       # 3D point clouds, meshes, and metric depth maps
+└── scripts/           # Utilities scripts
+```
+
+## 🚀 Release Status
+
+> - [x] `data/` — Full-resolution JPEG images + COLMAP
+> - [x] `data_4/` — Quarter-resolution JPEG images + COLMAP
+> - [x] `baseline_results/` - Baseline rendering results
+> - [ ] `data_arw/` — Sony RAW (ARW) sensor data
+> - [x] `pointclouds/` — 3D ground-truth geometry (point clouds, meshes, depth maps)
+
+
+## 🌧️ Capture Conditions
+
+| Condition | Description |
+|-----------|-------------|
+| `defocus_mild` | Mild defocus blur |
+| `defocus_strong` | Strong defocus blur |
+| `motion_mild` | Mild motion blur |
+| `motion_strong` | Strong motion blur |
+| `dynamic` | Dynamic objects in the scene |
+| `reflection` | Specular reflections |
+| `lowlight` | Low-light environment |
+| `smoke` | Smoke / particulate occlusion |
+| `varyexp` | Varying exposure |
+
+## 🏛️ Scenes
+
+Akikaze, BlueHawaii, Chocolate, Cupcake, GearWorks, Hinoki, Koharu, Laboratory, Limon, MilkCookie, Natsume, Popcorn, Sculpture, Shirohana, Ujikintoki
+
+---
+
+## 📸 `data/` — Full-Resolution JPEG Images
+
+Full-resolution JPEG images and corresponding COLMAP sparse reconstructions, organized by **condition → scene**.
+
+### Per-Scene Directory Layout
+
+```
+data/{condition}/{scene}/
+├── train/                    # Training images (~23–31 frames)
+│   ├── 0001.JPG
+│   └── ...
+├── val/                      # Validation images (~23–31 frames)
+│   └── ...
+├── test/                     # Test images (~4–6 frames)
+│   └── ...
+├── transforms_train.json     # Camera parameters & poses (training split)
+├── transforms_val.json       # Camera parameters & poses (validation split)
+├── transforms_test.json      # Camera parameters & poses (test split)
+├── point3d.ply               # COLMAP sparse 3D point cloud
+├── colmap2world.txt          # 4×4 COLMAP-to-world coordinate transform
+├── sparse/0/                 # COLMAP sparse reconstruction
+│   ├── cameras.bin / cameras.txt
+│   ├── images.bin / images.txt
+│   └── points3D.bin / points3D.txt
+├── distorted/sparse/0/       # Pre-undistortion COLMAP reconstruction
+└── stereo/                   # MVS configuration files
+```
+
+### 📐 `transforms.json` Format
+
+Each `transforms_*.json` file contains shared camera intrinsics and per-frame extrinsics following [`Blender Dataset`](https://docs.nerf.studio/quickstart/data_conventions.html) format, for example:
+
+```json
+{
+  "camera_angle_x": 1.295,
+  "camera_angle_y": 0.899,
+  "fl_x": 4778.31,
+  "fl_y": 4928.04,
+  "cx": 3649.23,
+  "cy": 2343.41,
+  "w": 7229.0,
+  "h": 4754.0,
+  "k1": 0, "k2": 0, "k3": 0, "k4": 0,
+  "p1": 0, "p2": 0,
+  "is_fisheye": false,
+  "aabb_scale": 2,
+  "frames": [
+    {
+      "file_path": "train/0001.JPG",
+      "sharpness": 25.72,
+      "transform_matrix": [[...], [...], [...], [...]]
+    }
+  ]
+}
+```
+
+All distortion coefficients are zero (images are pre-undistorted).
+
+### 🖼️ Image Specifications
+
+- **Format**: JPEG
+- **Resolution**: ~7000 × 4700 pixels (varies slightly across scenes)
+- **Camera**: Sony ILCE-7M4 (α7 IV)
+- **Camera Model**: PINHOLE (pre-undistorted)
+
+---
+
+## 📸 `data_4/` — Quarter-Resolution JPEG Images (Used for 2026 NTIRE-3DRR Challenge)
+
+Identical directory structure to `data/`, with images downsampled to **1/4 resolution** (~1800 × 1200 pixels). Camera intrinsics (`fl_x`, `fl_y`, `cx`, `cy`, `w`, `h`) in the `transforms.json` files are adjusted accordingly. All 9 capture conditions and their scenes are included.
+
+---
+
+## 📷 `data_arw/` — Sony RAW Data
+
+Sony ARW (TIFF-wrapped RAW) sensor data preserving full EXIF metadata.
+
+### Differences from `data/`
+
+- **Image format**: `.ARW` (~33–35 MB per frame)
+- **7 conditions available**: `defocus_mild`, `defocus_strong`, `dynamic`, `lowlight`, `reflection`, `smoke`, `varyexp` (motion blur conditions are **excluded**)
+
+### Per-Scene Directory Layout
+
+```
+data_arw/{condition}/{scene}/
+├── train/              # ARW raw images
+├── val/
+├── test/
+└── sparse/0/           # COLMAP sparse reconstruction
+```
+
+---
+
+## 📍 `pointclouds/` — 3D Ground Truth
+
+High-precision 3D geometry ground truth, organized directly by **scene name** (geometry is shared across capture conditions for the same scene).
+
+### Per-Scene Directory Layout
+
+```
+pointclouds/{scene}/
+├── cull_pointcloud.ply   # Culled point cloud (view-frustum trimmed)
+├── cull_mesh.ply         # Culled triangle mesh
+├── colmap2world.npy      # 4×4 COLMAP-to-world transform (NumPy format)
+└── depth/                # 16-bit Depth maps rendered from the mesh
+    ├── 0001.png
+    ├── 0002.png
+    └── ...
+```
+
+The `colmap2world.npy` matrix aligns COLMAP reconstructions to the world coordinate system of the ground-truth geometry. The same transform is also stored as `colmap2world.txt` in the corresponding `data/` directories.
+
+---
+
+## 📜 Citation
+
+```bibtex
+@article{liu2025realx3d,
+  title   = {RealX3D: A Physically-Degraded 3D Benchmark for Multi-view
+             Visual Restoration and Reconstruction},
+  author  = {Liu, Shuhong and Bao, Chenyu and Cui, Ziteng and Liu, Yun
+             and Chu, Xuangeng and Gu, Lin and Conde, Marcos V and
+             Umagami, Ryo and Hashimoto, Tomohiro and Hu, Zijian and others},
+  journal = {arXiv preprint arXiv:2512.23437},
+  year    = {2025}
+}
+```
+
+---
+
+## 📄 License
+
+This dataset is released under the [MIT License](https://opensource.org/licenses/MIT).

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