Update README.md

README.md

@@ -1,128 +1,6 @@
 ---
-title: StableSpann3R
+title: StableRecon
 app_file: app.py
 sdk: gradio
 sdk_version: 4.42.0
 ---
-# 3D Reconstruction with Spatial Memory
-
-### [Paper](https://arxiv.org/abs/2408.16061) | [Project Page](https://hengyiwang.github.io/projects/spanner) | [Video](https://hengyiwang.github.io/projects/spanner/videos/spanner_intro.mp4)
-
-> 3D Reconstruction with Spatial Memory <br />
-> [Hengyi Wang](https://hengyiwang.github.io/), [Lourdes Agapito](http://www0.cs.ucl.ac.uk/staff/L.Agapito/)<br />
-> arXiv 2024
-
-<p align="center">
-  <a href="">
-    <img src="./assets/spann3r_teaser_white.gif" alt="Logo" width="90%">
-  </a>
-</p>
-
-## Installation
-
-1. Clone Spann3R
-
-   ```
-   git clone https://github.com/HengyiWang/spann3r.git
-   cd spann3r
-   ```
-   
-2. Create conda environment
-
-   ```
-   conda create -n spann3r python=3.9 cmake=3.14.0
-   conda install pytorch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 pytorch-cuda=11.8 -c pytorch -c nvidia  # use the correct version of cuda for your system
-   
-   pip install -r requirements.txt
-   
-   # Open3D has a bug from 0.16.0, please use dev version
-   pip install -U -f https://www.open3d.org/docs/latest/getting_started.html open3d
-   ```
-
-3. Compile cuda kernels for RoPE
-
-   ```
-   cd croco/models/curope/
-   python setup.py build_ext --inplace
-   cd ../../../
-   ```
-
-4. Download the DUSt3R checkpoint
-
-   ```
-   mkdir checkpoints
-   cd checkpoints
-   # Download DUSt3R checkpoints
-   wget https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth
-   ```
-
-5. Download our [checkpoint](https://drive.google.com/drive/folders/1bqtcVf8lK4VC8LgG-SIGRBECcrFqM7Wy?usp=sharing) and place it under `./checkpoints`
-
-## Demo
-
-1. Download the [example data](https://drive.google.com/drive/folders/1bqtcVf8lK4VC8LgG-SIGRBECcrFqM7Wy?usp=sharing) (2 scenes from [map-free-reloc](https://github.com/nianticlabs/map-free-reloc)) and unzip it as `./examples`
-
-2. Run demo:
-
-   ```
-   python demo.py --demo_path ./examples/s00567 --kf_every 10 --vis
-   ```
-
-   For visualization `--vis`, it will give you a window to adjust the rendering view. Once you find the view to render, please click `space key` and close the window. The code will then do the rendering of the incremental reconstruction.
-
-
-
-## Training and Evaluation
-
-### Datasets
-
-We use Habitat, ScanNet++, ScanNet, ArkitScenes, Co3D, and BlendedMVS to train our model. Please refer to [data_preprocess.md](docs/data_preprocess.md).
-
-### Train
-
-Please use the following command to train our model:
-
-```
-torchrun --nproc_per_node 8 train.py --batch_size 4
-```
-
-### Eval
-
-Please use the following command to evaluate our model:
-
-```
-python eval.py
-```
-
-
-
-
-## Acknowledgement 
-
-Our code, data preprocessing pipeline, and evaluation scripts are based on several awesome repositories:
-
-- [DUSt3R](https://github.com/naver/dust3r)
-- [SplaTAM](https://github.com/spla-tam/SplaTAM)
-- [NeRFStudio](https://github.com/nerfstudio-project/nerfstudio)
-- [MVSNet](https://github.com/YoYo000/MVSNet)
-- [NICE-SLAM](https://github.com/cvg/nice-slam)
-- [NeuralRGBD](https://github.com/dazinovic/neural-rgbd-surface-reconstruction)
-- [SimpleRecon](https://github.com/nianticlabs/simplerecon)
-
-We thank the authors for releasing their code!
-
-The research presented here has been supported by a sponsored research award from Cisco Research and the UCL Centre for Doctoral Training in Foundational AI under UKRI grant number EP/S021566/1. This project made use of time on Tier 2 HPC facility JADE2, funded by EPSRC (EP/T022205/1).
-
-## Citation
-
-If you find our code or paper useful for your research, please consider citing:
-
-```
-@article{wang20243d,
-  title={3D Reconstruction with Spatial Memory},
-  author={Wang, Hengyi and Agapito, Lourdes},
-  journal={arXiv preprint arXiv:2408.16061},
-  year={2024}
-}
-```
-