Datasets:

ToferFish
/

RealX3D

	---
	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).