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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](https://dx.gl). Each dataset includes calibrated camera poses, depth maps, normal maps, binary masks, and point clouds — ready for [nerfstudio](https://docs.nerf.studio/) out of the box.
**10 objects × 196 views × 1024×1024 resolution × full sphere coverage.**
## Quick Start
```bash
# 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:
```bash
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**:
```json
{
"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](https://dx.gl/api/v/EJbs8npt2RVM/vCHDLxjWG65d/dataset) | [View](https://dx.gl/datasets/vCHDLxjWG65d) |
| 2 | Cash Register | electronics | [ZIP](https://dx.gl/api/v/EJbs8npt2RVM/JfjLRexr6J7z/dataset) | [View](https://dx.gl/datasets/JfjLRexr6J7z) |
| 3 | Drill | tool | [ZIP](https://dx.gl/api/v/EJbs8npt2RVM/A0dcsk7HHgAg/dataset) | [View](https://dx.gl/datasets/A0dcsk7HHgAg) |
| 4 | Fire Extinguisher | metallic | [ZIP](https://dx.gl/api/v/EJbs8npt2RVM/cLgyqM5mhQoq/dataset) | [View](https://dx.gl/datasets/cLgyqM5mhQoq) |
| 5 | LED Lightbulb | glass | [ZIP](https://dx.gl/api/v/EJbs8npt2RVM/ZuYmv3K9xN7u/dataset) | [View](https://dx.gl/datasets/ZuYmv3K9xN7u) |
| 6 | Measuring Tape | tool | [ZIP](https://dx.gl/api/v/EJbs8npt2RVM/qqvDYx7RtHZd/dataset) | [View](https://dx.gl/datasets/qqvDYx7RtHZd) |
| 7 | Modern Arm Chair | furniture | [ZIP](https://dx.gl/api/v/EJbs8npt2RVM/KLBJAuie9JaB/dataset) | [View](https://dx.gl/datasets/KLBJAuie9JaB) |
| 8 | Multi Cleaner 5L | product | [ZIP](https://dx.gl/api/v/EJbs8npt2RVM/79gDW15Gw9Ft/dataset) | [View](https://dx.gl/datasets/79gDW15Gw9Ft) |
| 9 | Potted Plant | organic | [ZIP](https://dx.gl/api/v/EJbs8npt2RVM/o4c5zRyGuT7W/dataset) | [View](https://dx.gl/datasets/o4c5zRyGuT7W) |
| 10 | Wet Floor Sign | plastic | [ZIP](https://dx.gl/api/v/EJbs8npt2RVM/tHdRul1GzzoU/dataset) | [View](https://dx.gl/datasets/tHdRul1GzzoU) |
All source models from [Polyhaven](https://polyhaven.com) (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:
```bash
python download_all.py --splats
```
Or view them directly:
- [DX.GL Splat Viewer](https://dx.gl/splat/index.html) (all 10 models, use ← → to browse)
- [SuperSplat Editor](https://superspl.at/editor) (drag-drop the .ply)
- nerfstudio viewer: `ns-viewer --load-config outputs/*/config.yml`
### Training Parameters
```bash
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](https://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](https://polyhaven.com). The rendered datasets inherit this license — use them for anything, no attribution required.
## Citation
```bibtex
@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](https://dx.gl/datasets/polyhaven-10)
- **Browse all datasets**: [dx.gl/datasets](https://dx.gl/datasets)
- **Pipeline details**: [dx.gl/for-research](https://dx.gl/for-research)
- **API documentation**: [dx.gl/portal/docs](https://dx.gl/portal/docs)
- **Generate your own**: [dx.gl/signup](https://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?
|