nerfbaselines 1.2.12

Reproducible evaluation of NeRF and 3DGS methods

NerfBaselines

NerfBaselines is a framework for evaluating and comparing existing NeRF and 3DGS methods. Currently, most official implementations use different dataset loaders, evaluation protocols, and metrics, which renders benchmarking difficult. Therefore, this project aims to provide a unified interface for running and evaluating methods on different datasets in a consistent way using the same metrics. But instead of reimplementing the methods, we use the official implementations and wrap them so that they can be run easily using the same interface.

Please visit the project page to see the results of implemented methods on dataset benchmarks.

🌐 Web  |  📄 Paper  |  📚 Docs

News

[11/08/2025] Added 3DGUT and 3DGRT methods.
[30/07/2025] Added Sparse-GS, DropGaussian methods and Mip-NeRF 360-sparse dataset.
[19/05/2025] Added Hierarchical-3DGS dataset and method, and Octree-GS method.
[18/05/2025] Added Taming-3DGS, 3DGS-MCMC, Student-Splatting-Scooping method.
[30/12/2024] Added a new viewer implementation.
[22/09/2024] Added mesh export for 2DGS, COLMAP, and GOF.
[17/09/2024] Moved project to nerfbaselines/nerfbaselines repository.
[16/09/2024] Added online demos and demo export for 3DGS-based methods. Check out the benchmark page.
[12/09/2024] Added gsplat, 2D Gaussian Splatting, Scaffold-GS, and COLMAP MVS methods.
[09/09/2024] Method and Dataset API refac in v1.2.x to simplify usage.
[28/08/2024] Implemented faster communication protocols using shared memory.
[20/08/2024] Added documentation page.

Getting started

Start by installing the nerfbaselines pip package on your host system.

pip install nerfbaselines

Now you can use the nerfbaselines cli to interact with NerfBaselines.

The next step is to choose the backend which will be used to install different methods. At the moment there are the following backends implemented:

• docker: Offers good isolation, requires docker (with NVIDIA container toolkit) to be installed and the user to have access to it (being in the docker user group).
• apptainer: Similar level of isolation as docker, but does not require the user to have privileged access.
• conda (default): Does not require docker/apptainer to be installed, but does not offer the same level of isolation and some methods require additional dependencies to be installed. Also, some methods are not implemented for this backend because they rely on dependencies not found on conda.
• python: Will run everything directly in the current environment. Everything needs to be installed in the environment for this backend to work.

The backend can be set as the --backend <backend> argument or using the NERFBASELINES_BACKEND environment variable.

Downloading data

For some datasets, e.g. Mip-NeRF 360, NerfStudio, Blender, or Tanks and Temples, the datasets can be downloaded automatically. You can specify the argument --data external://dataset/scene during training or download the dataset beforehand by running nerfbaselines download-dataset external://dataset/scene. Examples:

# Downloads the garden scene to the cache folder.
nerfbaselines download-dataset external://mipnerf360/garden

# Downloads all nerfstudio scenes to the cache folder.
nerfbaselines download-dataset external://nerfstudio

# Downloads kithen scene to folder kitchen
nerfbaselines download-dataset external://mipnerf360/kitchen -o kitchen

Training

To start the training, use the nerfbaselines train --method <method> --data <data> command. Use --help argument to learn about all implemented methods and supported features.

Rendering

The nerfbaselines render --checkpoint <checkpoint> command can be used to render images from a trained checkpoint. Again, use --help to learn about the arguments.

In order to render a camera trajectory (e.g., created using the interactive viewer), use the following command command:

nerfbaselines render-trajectory --checkpoint <checkpoint> --trajectory <trajectory> --output <output.mp4>

Interactive viewer

Given a trained checkpoint, the interactive viewer can be launched as follows:

nerfbaselines viewer --checkpoint <checkpoin> --data <dataset>

Even though the argument --data <dataset> is optional, it is recommended, as the camera poses are used to perform gravity alignment and rescaling for a better viewing experience. It also enables visualizing the input camera frustums.

Results

In this section, we present results of implemented methods on standard benchmark datasets. For detailed results, visit the project page: https://nerfbaselines.github.io

Mip-NeRF 360

Mip-NeRF 360 is a collection of four indoor and five outdoor object-centric scenes. The camera trajectory is an orbit around the object with fixed elevation and radius. The test set takes each n-th frame of the trajectory as test views. Detailed results are available on the project page: https://nerfbaselines.github.io/mipnerf360

Method	PSNR	SSIM	LPIPS (VGG)	Time	GPU mem.
Zip-NeRF	28.553	0.829	0.218	5h 30m 20s	26.8 GB
3DGS-MCMC	27.983	0.835	0.224	41m 11s	28.9 GB
Scaffold-GS	27.714	0.813	0.262	23m 28s	8.7 GB
Mip-NeRF 360	27.681	0.792	0.272	30h 14m 36s	33.6 GB
Mip-Splatting	27.492	0.815	0.258	25m 37s	11.0 GB
Gaussian Splatting	27.434	0.814	0.257	23m 25s	11.1 GB
Gaussian Opacity Fields	27.421	0.826	0.234	1h 3m 54s	28.4 GB
gsplat	27.412	0.815	0.256	29m 19s	8.3 GB
Octree-GS	27.397	0.811	0.264	28m 3s	8.5 GB
Taming 3DGS	27.217	0.793	0.305	5m 59s	8.7 GB
PGSR	27.199	0.819	0.233	39m 58s	14.3 GB
3DGUT	27.041	0.812	0.252	34m 34s	14.9 GB
H3DGS	26.927	0.790	0.269	55m 29s	9.1 GB
2D Gaussian Splatting	26.815	0.796	0.297	31m 10s	13.2 GB
NerfStudio	26.388	0.731	0.343	19m 30s	5.9 GB
Instant NGP	25.507	0.684	0.398	3m 54s	7.8 GB
COLMAP	16.670	0.445	0.590	2h 52m 55s	0 MB

Blender

Blender (nerf-synthetic) is a synthetic dataset used to benchmark NeRF methods. It consists of 8 scenes of an object placed on a white background. Cameras are placed on a semi-sphere around the object. Scenes are licensed under various CC licenses. Detailed results are available on the project page: https://nerfbaselines.github.io/blender

Method	PSNR	SSIM	LPIPS (VGG)	Time	GPU mem.
Zip-NeRF	33.670	0.973	0.036	5h 21m 57s	26.2 GB
3DGS-MCMC	33.637	0.971	0.036	9m 8s	4.5 GB
3DGUT	33.476	0.969	0.036	7m 16s	4.7 GB
Gaussian Opacity Fields	33.451	0.969	0.038	18m 26s	3.1 GB
Mip-Splatting	33.330	0.969	0.039	6m 49s	2.7 GB
Gaussian Splatting	33.308	0.969	0.037	6m 6s	3.1 GB
PGSR	33.274	0.968	0.039	8m 20s	3.9 GB
TensoRF	33.172	0.963	0.051	10m 47s	16.4 GB
Scaffold-GS	33.080	0.966	0.048	7m 4s	3.7 GB
K-Planes	32.265	0.961	0.062	23m 58s	4.6 GB
Instant NGP	32.198	0.959	0.055	2m 23s	2.6 GB
Tetra-NeRF	31.951	0.957	0.056	6h 53m 20s	29.6 GB
gsplat	31.471	0.966	0.054	14m 45s	2.8 GB
Mip-NeRF 360	30.345	0.951	0.060	3h 29m 39s	114.8 GB
NerfStudio	29.191	0.941	0.095	9m 38s	3.6 GB
NeRF	28.723	0.936	0.092	23h 26m 30s	10.2 GB
COLMAP	12.123	0.766	0.214	1h 20m 34s	0 MB

Tanks and Temples

Tanks and Temples is a benchmark for image-based 3D reconstruction. The benchmark sequences were acquired outside the lab, in realistic conditions. Ground-truth data was captured using an industrial laser scanner. The benchmark includes both outdoor scenes and indoor environments. The dataset is split into three subsets: training, intermediate, and advanced. Detailed results are available on the project page: https://nerfbaselines.github.io/tanksandtemples

Method	PSNR	SSIM	LPIPS	Time	GPU mem.
Zip-NeRF	24.628	0.840	0.131	5h 44m 9s	26.6 GB
Mip-Splatting	23.930	0.833	0.166	15m 56s	7.3 GB
Gaussian Splatting	23.827	0.831	0.165	13m 48s	6.9 GB
PGSR	23.209	0.832	0.146	18m 59s	10.4 GB
Gaussian Opacity Fields	22.395	0.825	0.172	41m 21s	24.1 GB
NerfStudio	22.043	0.743	0.270	19m 27s	3.7 GB
Instant NGP	21.623	0.712	0.340	4m 27s	4.1 GB
2D Gaussian Splatting	21.535	0.768	0.281	15m 47s	7.2 GB
COLMAP	11.919	0.436	0.606	5h 16m 11s	0 MB

Implementation status

Method	Blender	Hierarchical 3DGS	LLFF	Mip-NeRF 360	Mip-NeRF 360 Sparse	Nerfstudio	Photo Tourism	SeaThru-NeRF	Tanks and Temples	Zip-NeRF
2D Gaussian Splatting	🥇 gold	❔	❔	🥇 gold	❔	❔	❔	🥇 gold	🥈 silver	❔
3DGRT	❔	❔	❔	❔	❔	❔	❔	❔	❔	❔
3DGS-MCMC	🥈 silver	❔	❔	🥇 gold	❔	❔	❔	🥇 gold	🥇 gold	❔
3DGUT	❔	❔	❔	❔	❔	❔	❔	❔	❔	❔
CamP	❔	❔	❔	❔	❔	❔	❔	❔	❔	❔
COLMAP	🥇 gold	❔	❔	🥇 gold	❔	🥇 gold	❔	❔	🥇 gold	❔
DropGaussian	❔	❔	❔	❔	❔	❔	❔	❔	❔	❔
Gaussian Opacity Fields	🥇 gold	❔	❔	🥇 gold	❔	❔	❔	❔	🥇 gold	❔
Gaussian Splatting	🥇 gold	❔	❔	🥇 gold	❔	❔	❔	🥇 gold	🥇 gold	❔
GS-W	❔	❔	❔	❔	❔	❔	❔	❔	❔	❔
gsplat	🥇 gold	❔	❔	🥇 gold	❔	❔	🥇 gold	❔	🥇 gold	❔
H3DGS	❔	❔	❔	❔	❔	❔	❔	❔	❔	❔
Instant NGP	🥇 gold	❔	❔	🥇 gold	❔	🥇 gold	❔	❔	🥇 gold	❔
K-Planes	🥇 gold	❔	❔	❔	❔	❔	🥈 silver	❔	❔	❔
Mip-NeRF 360	🥇 gold	❔	❔	🥇 gold	❔	❔	❔	❔	🥇 gold	❔
Mip-Splatting	🥇 gold	❔	❔	🥇 gold	❔	❔	❔	🥇 gold	🥇 gold	❔
NeRF	🥇 gold	❔	❔	❔	❔	❔	❔	❔	❔	❔
NeRF On-the-go	❔	❔	❔	❔	❔	❔	❔	❔	❔	❔
NeRF-W (reimplementation)	❔	❔	❔	❔	❔	❔	🥇 gold	❔	❔	❔
NerfStudio	🥇 gold	❔	❔	🥇 gold	❔	❔	❔	❔	🥇 gold	❔
Octree-GS	❔	❔	❔	❔	❔	❔	❔	❔	❔	❔
PGSR	❔	❔	❔	🥇 gold	❔	❔	❔	❔	🥇 gold	❔
Scaffold-GS	🥇 gold	❔	❔	🥇 gold	❔	❔	❔	🥇 gold	🥇 gold	❔
SeaThru-NeRF	❔	❔	❔	❔	❔	❔	❔	🥇 gold	❔	❔
SparseGS	❔	❔	❔	❔	❔	❔	❔	❔	❔	❔
Student Splatting Scooping	❔	❔	❔	❔	❔	❔	❔	❔	❔	❔
Taming 3DGS	🥇 gold	❔	❔	🥇 gold	❔	❔	❔	❔	🥇 gold	❔
TensoRF	🥇 gold	❔	🥇 gold	❌	❔	❔	❔	❔	❔	❔
Tetra-NeRF	🥈 silver	❔	❔	🥈 silver	❔	❔	❔	❔	❔	❔
WildGaussians	❔	❔	❔	❔	❔	❔	🥇 gold	❔	❔	❔
Zip-NeRF	🥇 gold	❔	❌	🥇 gold	❔	🥇 gold	❔	❔	❔	❔

Contributing

Contributions are very much welcome. Please open a PR with a dataset/method/feature that you want to contribute. The goal of this project is to slowly expand by implementing more and more methods.

Citation

If you use this project in your research, please cite the following paper:

@article{kulhanek2024nerfbaselines,
  title={NerfBaselines: Consistent and Reproducible Evaluation of Novel View Synthesis Methods},
  author={Jonas Kulhanek and Torsten Sattler},
  year={2024},
  journal={arXiv},
}

License

This project is licensed under the MIT license Each implemented method is licensed under the license provided by the authors of the method. For the currently implemented methods, the following licenses apply:

• 2D Gaussian Splatting: custom, research only
• 3DGRT: Apache 2.0
• 3DGS-MCMC: custom, research only
• 3DGUT: Apache 2.0
• CamP: Apache 2.0
• COLMAP: BSD
• DropGaussian: custom, research only, Apache 2.0
• Gaussian Opacity Fields: custom, research only
• Gaussian Splatting: custom, research only
• GS-W: unknown
• gsplat: Apache 2.0
• H3DGS: custom, research only, custom, research only
• Instant NGP: custom, research only
• K-Planes: BSD 3
• Mip-NeRF 360: Apache 2.0
• Mip-Splatting: custom, research only
• NeRF-W (reimplementation): MIT
• NeRF: MIT
• NerfStudio: Apache 2.0
• Octree-GS: custom, research only
• PGSR: custom, research only
• Scaffold-GS: custom, research only
• SeaThru-NeRF: Apache 2.0
• SparseGS: custom, research only
• Student Splatting Scooping: custom, research only, GPL-2.0
• Taming 3DGS: MIT, custom, research only
• TensoRF: MIT
• Tetra-NeRF: MIT
• WildGaussians: MIT, custom, research only
• Zip-NeRF: Apache 2.0

Acknowledgements

A big thanks to the authors of all implemented methods for the great work they have done. We would also like to thank the authors of NerfStudio. We also thank Mark Kellogg for the 3DGS web renderer. This work was supported by the Czech Science Foundation (GAČR) EXPRO (grant no. 23-07973X), the Grant Agency of the Czech Technical University in Prague (grant no. SGS24/095/OHK3/2T/13), and by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254).