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[CVPR'23] Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM
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# Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM
### [Paper](https://arxiv.org/pdf/2304.14377.pdf) | [Project Page](https://hengyiwang.github.io/projects/CoSLAM) | [Video](https://hengyiwang.github.io/projects/Co-SLAM/videos/presentation.mp4)
> Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM <br />
> [Hengyi Wang](https://hengyiwang.github.io/), [Jingwen Wang](https://jingwenwang95.github.io/), [Lourdes Agapito](http://www0.cs.ucl.ac.uk/staff/L.Agapito/)<br />
> CVPR 2023
<p align="center">
<a href="">
<img src="./media/coslam_teaser.gif" alt="Logo" width="80%">
</a>
</p>
This repository contains the code for the paper Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM, a neural SLAM method that perform real-time camera tracking and dense reconstruction based on a joint encoding.
## Update
- [x] Code for Co-SLAM [2023-5-12]
- [x] Code for offline RGB-D reconstruction [click here](https://github.com/HengyiWang/Hybrid-Surf). [2023-5-12]
- [x] Code for evaluation strategy, performance analysis [click here](https://github.com/JingwenWang95/neural_slam_eval). [2023-5-18]
- [x] Tutorials on params & creating sequences using iPhone/iPad Pro [click here](./DOCUMENTATION.md). [2023-5-26]
- [ ] Tutorials on creating sequences using RealSense
## Installation
Please follow the instructions below to install the repo and dependencies.
```bash
git clone https://github.com/HengyiWang/Co-SLAM.git
cd Co-SLAM
```
### Install the environment
```bash
# Create conda environment
conda create -n coslam python=3.7
conda activate coslam
# Install the pytorch first (Please check the cuda version)
pip install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu113/torch_stable.html
# Install all the dependencies via pip (Note here pytorch3d and tinycudann requires ~10min to build)
pip install -r requirements.txt
# Build extension (marching cubes from neuralRGBD)
cd external/NumpyMarchingCubes
python setup.py install
```
For tinycudann, if you cannot access network when you use GPUs, you can also try build from source as below:
```bash
# Build tinycudann
git clone --recursive https://github.com/nvlabs/tiny-cuda-nn
# Try this version if you cannot use the latest version of tinycudann
#git reset --hard 91ee479d275d322a65726435040fc20b56b9c991
cd tiny-cuda-nn/bindings/torch
python setup.py install
```
## Dataset
#### Replica
Download the sequences of the Replica Dataset generated by the authors of iMAP into `./data/Replica` folder.
```bash
bash scripts/download_replica.sh # Released by authors of NICE-SLAM
```
#### ScanNet
Please follow the procedure on [ScanNet](http://www.scan-net.org/) website, and extract color & depth frames from the `.sens` file using the [code](https://github.com/ScanNet/ScanNet/blob/master/SensReader/python/reader.py).
#### Synthetic RGB-D dataset
Download the sequences of the synethetic RGB-D dataset generated by the authors of neuralRGBD into `./data/neural_rgbd_data` folder. We exclude the scenes with NaN poses generated by BundleFusion.
```bash
bash scripts/download_rgbd.sh
```
#### TUM RGB-D
Download 3 sequences of TUM RGB-D dataset into `./data/TUM` folder.
```bash
bash scripts/download_tum.sh
```
## Run
You can run Co-SLAM using the code below:
```
python coslam.py --config './configs/{Dataset}/{scene}.yaml
```
You can run Co-SLAM with multi-processing using the code below:
```
python coslam_mp.py --config './configs/{Dataset}/{scene}.yaml
```
## Evaluation
We employ a slightly different evaluation strategy to measure the quality of the reconstruction, you can find out the code [here](https://github.com/JingwenWang95/neural_slam_eval). Note if you want to follow the evaluation protocol of NICE-SLAM, please refer to our supplementary material for detailed parameters setting.
## Acknowledgement
WeExcerpt of 5,200 characters
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