README.md

---
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
> - [ ] `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).