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README.md

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 ---
 license: apache-2.0
+pipeline_tag: image-to-3d
 ---
+
+# R³: 3D Reconstruction via Relative Regression
+
+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.
+
+[**Project Page**](https://kevinxu02.github.io/r3-site/) | [**Paper**](https://huggingface.co/papers/2605.26519) | [**GitHub**](https://github.com/KevinXu02/R3)
+
+## Quick Start
+
+### Installation
+
+```bash
+conda env create -f environment.yml
+conda activate r3
+pip install -e .
+```
+
+Alternatively, if you already have a CUDA-enabled PyTorch environment:
+
+```bash
+pip install -r requirements.txt
+pip install -e .
+```
+
+### Run the Demo
+
+You can run inference on a sequence and visualize it using the following command:
+
+```bash
+python demo.py --seq_path examples/indoor --no_viewer
+```
+
+Presets for common scenarios:
+
+```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
+```
+
+## Citation
+
+If R³ is useful in your research or projects, please cite:
+
+```bibtex
+@article{r3_2026,
+  title  = {R^3: 3D Reconstruction via Relative Regression},
+  author = {Anonymous},
+  year   = {2026},
+  note   = {Paper coming soon}
+}
+```