license: cc0-1.0
task_categories:
- image-to-3d
- depth-estimation
tags:
- nerf
- 3d-gaussian-splatting
- 3dgs
- nerfstudio
- multi-view
- depth-maps
- normal-maps
- point-cloud
- computer-vision
- 3d-reconstruction
pretty_name: DX.GL Multi-View Datasets
size_categories:
- 1K<n<10K
DX.GL Multi-View Datasets for NeRF & 3D Gaussian Splatting
Multi-view training datasets rendered from CC0 3D models via DX.GL. Each dataset includes calibrated camera poses, depth maps, normal maps, binary masks, and point clouds — ready for nerfstudio out of the box.
10 objects × 196 views × 1024×1024 resolution × full sphere coverage.
Quick Start
# Download a dataset (Apple, 196 views, 1024x1024)
wget https://dx.gl/api/v/EJbs8npt2RVM/vCHDLxjWG65d/dataset -O apple.zip
unzip apple.zip -d apple
# Train with nerfstudio
pip install nerfstudio
ns-train splatfacto --data ./apple \
--max-num-iterations 20000 \
--pipeline.model.sh-degree 3 \
--pipeline.model.background-color white
Or use the download script:
pip install requests
python download_all.py
What's in Each Dataset ZIP
dataset/
├── images/ # RGB frames (PNG, transparent background)
│ ├── frame_00000.png
│ └── ...
├── depth/ # 8-bit grayscale depth maps
├── depth_16bit/ # 16-bit grayscale depth maps (higher precision)
├── normals/ # World-space normal maps
├── masks/ # Binary alpha masks
├── transforms.json # Camera poses (nerfstudio / instant-ngp format)
└── points3D.ply # Sparse point cloud for initialization
transforms.json Format
Compatible with both nerfstudio and instant-ngp:
{
"camera_angle_x": 0.857,
"camera_angle_y": 0.857,
"fl_x": 693.5,
"fl_y": 693.5,
"cx": 400,
"cy": 400,
"w": 800,
"h": 800,
"depth_near": 0.85,
"depth_far": 2.35,
"ply_file_path": "points3D.ply",
"frames": [
{
"file_path": "images/frame_00000.png",
"depth_file_path": "depth/frame_00000.png",
"normal_file_path": "normals/frame_00000.png",
"mask_file_path": "masks/frame_00000.png",
"transform_matrix": [[...], [...], [...], [0, 0, 0, 1]]
}
]
}
Specs
| Property | Value |
|---|---|
| Views | 196 per object |
| Resolution | 1024×1024 |
| Coverage | Full sphere (±89° elevation) |
| Point cloud | ~200k points |
| Camera distribution | Fibonacci golden-angle spiral |
| Background | Transparent (RGBA) |
| Lighting | Studio HDRI + directional lights |
Camera Distribution
Views are distributed on a full sphere (±89° elevation) using a golden-angle Fibonacci spiral. The distribution is uniform in solid angle — more views near the equator, fewer near the poles — optimized for NeRF/3DGS training.
Objects
| # | Object | Category | Download | Browse |
|---|---|---|---|---|
| 1 | Apple | organic | ZIP | View |
| 2 | Cash Register | electronics | ZIP | View |
| 3 | Drill | tool | ZIP | View |
| 4 | Fire Extinguisher | metallic | ZIP | View |
| 5 | LED Lightbulb | glass | ZIP | View |
| 6 | Measuring Tape | tool | ZIP | View |
| 7 | Modern Arm Chair | furniture | ZIP | View |
| 8 | Multi Cleaner 5L | product | ZIP | View |
| 9 | Potted Plant | organic | ZIP | View |
| 10 | Wet Floor Sign | plastic | ZIP | View |
All source models from Polyhaven (CC0).
Pre-trained 3DGS Splats
We include pre-trained Gaussian Splat .ply files (nerfstudio splatfacto, 20k iterations, SH degree 3) for each object. Download them with:
python download_all.py --splats
Or view them directly:
- DX.GL Splat Viewer (all 10 models, use ← → to browse)
- SuperSplat Editor (drag-drop the .ply)
- nerfstudio viewer:
ns-viewer --load-config outputs/*/config.yml
Training Parameters
ns-train splatfacto --data ./dataset \
--max-num-iterations 20000 \
--pipeline.model.sh-degree 3 \
--pipeline.model.background-color white \
--pipeline.model.cull-alpha-thresh 0.2 \
--pipeline.model.densify-size-thresh 0.005 \
--pipeline.model.use-scale-regularization True \
--pipeline.model.max-gauss-ratio 5.0
Training time: ~10 minutes on RTX 4000 Pro Ada (70W) at the 196×1024 tier.
Rendering Pipeline
Datasets are rendered using DX.GL's cloud GPU rendering pipeline:
- Lighting: Studio HDRI environment with PBR materials
- Camera: Fibonacci golden-angle sphere distribution
- Depth: Tight near/far planes from model bounding sphere for maximum precision
- Point cloud: Back-projected from depth maps, ~1000 points per view
- Background: Transparent (RGBA)
Modalities
| Modality | Format | Notes |
|---|---|---|
| RGB | PNG, RGBA | Transparent background, PBR-lit |
| Depth (8-bit) | PNG, grayscale | Normalized to near/far range |
| Depth (16-bit) | PNG, grayscale | RG-encoded, higher precision |
| Normals | PNG, RGB | World-space, MeshNormalMaterial |
| Masks | PNG, grayscale | Binary alpha from RGB alpha channel |
| Point Cloud | PLY, binary | XYZ + RGB, ~100k points |
| Camera Poses | JSON | 4×4 camera-to-world matrices |
License
All source 3D models are CC0 (public domain) from Polyhaven. The rendered datasets inherit this license — use them for anything, no attribution required.
Citation
@misc{dxgl_multiview_2026,
title = {DX.GL Multi-View Datasets for NeRF and 3D Gaussian Splatting},
author = {DXGL},
year = {2026},
url = {https://huggingface.co/datasets/dxgl/multiview-datasets},
note = {Multi-view datasets with depth, normals, masks, and point clouds. Rendered via DX.GL.}
}
Links
- This collection: dx.gl/datasets/polyhaven-10
- Browse all datasets: dx.gl/datasets
- Pipeline details: dx.gl/for-research
- API documentation: dx.gl/portal/docs
- Generate your own: dx.gl/signup (2 free renders included)
Feedback
We're actively improving the rendering pipeline. If you find issues with depth accuracy, mask quality, camera calibration, or view distribution — please open a Discussion on this repo. Specific feedback we're looking for:
- Depth map accuracy at object edges
- Mask quality for transparent/reflective materials
- Point cloud alignment with RGB views
- View distribution quality for your training method
- Missing modalities or metadata
- Any other issues or suggestions?