From None to All:
Self-Supervised 3D Reconstruction via Novel View Synthesis

Ranran Huang · Weixun Luo · Ye Mao · Krystian Mikolajczyk

Paper | Project Page

NAS3R is a self-supervised feed-forward framework that jointly learns explicit 3D geometry and camera parameters with no ground-truth annotations and no pretrained priors.

Table of Contents

1. Installation
2. Pre-trained Checkpoints
3. Camera Conventions
4. Datasets
5. Running the Code
6. Acknowledgements
7. Citation

Installation

1. Clone NAS3R.

git clone --recurse-submodules git@github.com:ranrhuang/NAS3R.git
cd NAS3R

2. Create the environment, here we show an example using conda.

conda create -n nas3r python=3.11 -y
conda activate nas3r
pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt --no-build-isolation
pip install -e submodules/diff-gaussian-rasterization --no-build-isolation

Pre-trained Checkpoints

Our models are hosted on Hugging Face 🤗

Model name	Training resolutions	Training data	Training settings
re10k_nas3r.ckpt	256x256	re10k	2 views
re10k_nas3r_multiview.ckpt	256x256	re10k	2-10 views
re10k_nas3r_pretrained.ckpt	256x256	re10k	2 views, initialized by VGGT
re10k_nas3r_pretrained-I.ckpt	256x256	re10k	2 views, initialized by VGGT, incorporate GT intrinsics

We assume the downloaded weights are located in the checkpoints directory.

Datasets

Please refer to DATASETS.md for dataset preparation.

Running the Code

Training

# 2 view on NAS3R (VGGT-based architecture), for multi-view training, modify view_sampler.num_context_views
python -m src.main +experiment=nas3r/random/re10k wandb.mode=online wandb.name=nas3r_re10k


# Initialized by pretrained VGGT weights for better performance and stability.
python -m src.main +experiment=nas3r/pretrained/re10k wandb.mode=online wandb.name=nas3r_re10k_pretrained

# Initialized by pretrained VGGT weights and incorporate GT intrinsics for better performance and stability. 
python -m src.main +experiment=nas3r/pretrained/re10k-I wandb.mode=online wandb.name=nas3r_re10k_pretrained-I

Evaluation

Novel View Synthesis and Pose Estimation on NAS3R (VGGT-based architecture)

# RealEstate10K on NAS3R
python -m src.main +experiment=nas3r/random/re10k mode=test wandb.name=re10k \
    dataset/view_sampler@dataset.re10k.view_sampler=evaluation \
    dataset.re10k.view_sampler.index_path=assets/evaluation_index_re10k.json \
    checkpointing.load=./checkpoints/re10k_nas3r.ckpt \
    test.save_image=false 

# RealEstate10K on NAS3R, 10 view
python -m src.main +experiment=nas3r/random/re10k mode=test wandb.name=re10k \
    dataset/view_sampler@dataset.re10k.view_sampler=evaluation \
    dataset.re10k.view_sampler.index_path=assets/evaluation_index_re10k.json \
    dataset.re10k.view_sampler.num_context_views=10 \
    checkpointing.load=./checkpoints/re10k_nas3r_multiview.ckpt \
    test.save_image=false 


# RealEstate10K on NAS3R pretrained from VGGT
python -m src.main +experiment=nas3r/random/re10k mode=test wandb.name=re10k \
    dataset/view_sampler@dataset.re10k.view_sampler=evaluation \
    dataset.re10k.view_sampler.index_path=assets/evaluation_index_re10k.json \
    checkpointing.load=./checkpoints/re10k_nas3r_pretrained.ckpt \
    test.save_image=false 

# RealEstate10K on NAS3R pretrained from VGGT, incorporate GT intrinsics
python -m src.main +experiment=nas3r/random/re10k-I mode=test wandb.name=re10k \
    dataset/view_sampler@dataset.re10k.view_sampler=evaluation \
    dataset.re10k.view_sampler.index_path=assets/evaluation_index_re10k.json \
    checkpointing.load=./checkpoints/re10k_nas3r_pretrained-I.ckpt \
    test.save_image=false 

Camera Conventions

We follow the pixelSplat camera system. The camera intrinsic matrices are normalized (the first row is divided by image width, and the second row is divided by image height). The camera extrinsic matrices are OpenCV-style camera-to-world matrices ( +X right, +Y down, +Z camera looks into the screen).

Acknowledgements

This project is built upon these excellent repositories:SPFSplatV2, SPFSplat, NoPoSplat, pixelSplat, DUSt3R, and CroCo. We thank the original authors for their excellent work.

Citation

@article{huang2026nas3r,
      title={From None to All: Self-Supervised 3D Reconstruction via Novel View Synthesis} ,
      author={Ranran Huang and Weixun Luo and Ye Mao and Krystian Mikolajczyk},
      journal={arXiv preprint arXiv: 2603.27455},
      year={2026}
}