10 multi-view training datasets rendered from CC0 3D models. Ready for NeRF, 3D Gaussian Splatting, and 3D reconstruction research.
Each dataset was trained with nerfstudio splatfacto (20k iterations). Drag to orbit, scroll to zoom.
Use ← → to browse all 10 models
| Objects | 10 |
| Views | 196 per object |
| Resolution | 1024 × 1024 |
| Coverage | Full sphere (±89° elevation) |
| Distribution | Fibonacci golden-angle spiral |
| Point cloud | ~200k points |
| Background | Transparent (RGBA) |
| Modalities | RGB, depth 8-bit, depth 16-bit, normals, masks, point cloud, camera poses |
| Format | nerfstudio / instant-ngp (transforms.json) |
| License | CC0 (public domain) |
| Source | Polyhaven |
196 views on a full sphere (±89° elevation), Fibonacci golden-angle spiral distribution
pip install requests
python download_all.py # all datasets
python download_all.py --splats # + pre-trained splats
python download_all.py --object apple # single object ns-train splatfacto --data ./dxgl-datasets/apple \
--max-num-iterations 20000 \
--pipeline.model.sh-degree 3 \
--pipeline.model.background-color white \
--pipeline.model.cull-alpha-thresh 0.2 \
--pipeline.model.use-scale-regularization True python train_all.py # ~100 min on RTX 4000 Pro Ada
python train_all.py --object apple # single object (~10 min) dataset/ ├── images/ 196 RGB frames (PNG, RGBA) ├── depth/ 8-bit grayscale depth maps ├── depth_16bit/ 16-bit grayscale depth maps ├── normals/ World-space normal maps ├── masks/ Binary alpha masks ├── transforms.json Camera poses (nerfstudio format) └── points3D.ply Sparse point cloud (~200k pts)
@misc{dxgl_polyhaven10_2026,
title = {Polyhaven 10 — Multi-View Datasets for NeRF and 3D Gaussian Splatting},
author = {DXGL},
year = {2026},
url = {https://dx.gl/datasets/polyhaven-10},
note = {10 CC0 objects, 196 views at 1024x1024, full sphere coverage. Rendered via DX.GL.}
} Upload any GLB model and render calibrated multi-view datasets with the DX.GL pipeline. 10 model uploads with shareable turntable videos included.