| --- |
| license: apache-2.0 |
| pipeline_tag: image-to-3d |
| --- |
| |
| # R³: 3D Reconstruction via Relative Regression |
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| R³ is a feed-forward geometry foundation model that reconstructs camera poses and dense geometry from arbitrarily long video streams via relative-pose regression. Instead of regressing every camera in one global frame, R³ predicts confidence-weighted pairwise relative poses on top of a Depth Anything 3 backbone, then assembles a consistent global trajectory downstream. |
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| [**Project Page**](https://kevinxu02.github.io/r3-site/) | [**Paper**](https://huggingface.co/papers/2605.26519) | [**GitHub**](https://github.com/KevinXu02/R3) |
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| ## Quick Start |
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| ### Installation |
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|
| ```bash |
| conda env create -f environment.yml |
| conda activate r3 |
| pip install -e . |
| ``` |
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| Alternatively, if you already have a CUDA-enabled PyTorch environment: |
|
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| ```bash |
| pip install -r requirements.txt |
| pip install -e . |
| ``` |
|
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| ### Run the Demo |
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| You can run inference on a sequence and visualize it using the following command: |
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| ```bash |
| python demo.py --seq_path examples/indoor --no_viewer |
| ``` |
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| Presets for common scenarios: |
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| ```bash |
| python demo.py --mode local # indoor scenes, small coverage |
| python demo.py --mode long # long trajectories, large outdoor scenes |
| python demo.py --mode strided # temporally strided video |
| ``` |
|
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| ## Citation |
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| If R³ is useful in your research or projects, please cite: |
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| ```bibtex |
| @article{r3_2026, |
| title = {R^3: 3D Reconstruction via Relative Regression}, |
| author = {Anonymous}, |
| year = {2026}, |
| note = {Paper coming soon} |
| } |
| ``` |
