base on SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM (CVPR 2024) <!-- PROJECT LOGO --> <p align="center"> <h1 align="center">SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM</h1> <h3 align="center">CVPR 2024</h3> <p align="center"> <a href="https://nik-v9.github.io/"><strong>Nikhil Keetha</strong></a> · <a href="https://jaykarhade.github.io/"><strong>Jay Karhade</strong></a> · <a href="https://krrish94.github.io/"><strong>Krishna Murthy Jatavallabhula</strong></a> · <a href="https://gengshan-y.github.io/"><strong>Gengshan Yang</strong></a> · <a href="https://theairlab.org/team/sebastian/"><strong>Sebastian Scherer</strong></a> <br> <a href="https://www.cs.cmu.edu/~deva/"><strong>Deva Ramanan</strong></a> · <a href="https://www.vision.rwth-aachen.de/person/216/"><strong>Jonathon Luiten</strong></a> </p> <h3 align="center"><a href="https://arxiv.org/pdf/2312.02126.pdf">Paper</a> | <a href="https://youtu.be/jWLI-OFp3qU">Video</a> | <a href="https://spla-tam.github.io/">Project Page</a></h3> <div align="center"></div> </p> <p align="center"> <a href=""> <img src="./assets/1.gif" alt="Logo" width="100%"> </a> </p> <br> ## Stay Tuned for a Faster and Better Variant of SplaTAM! <!-- TABLE OF CONTENTS --> <details open="open" style='padding: 10px; border-radius:5px 30px 30px 5px; border-style: solid; border-width: 1px;'> <summary>Table of Contents</summary> <ol> <li> <a href="#installation">Installation</a> </li> <li> <a href="#demo">Online Demo</a> </li> <li> <a href="#usage">Usage</a> </li> <li> <a href="#downloads">Downloads</a> </li> <li> <a href="#benchmarking">Benchmarking</a> </li> <li> <a href="#acknowledgement">Acknowledgement</a> </li> <li> <a href="#citation">Citation</a> </li> <li> <a href="#developers">Developers</a> </li> </ol> </details> ## Installation ##### (Recommended) SplaTAM has been benchmarked with Python 3.10, Torch 1.12.1 & CUDA=11.6. However, Torch 1.12 is not a hard requirement and the code has also been tested with other versions of Torch and CUDA such as Torch 2.3.0 & CUDA 12.1. The simplest way to install all dependences is to use [anaconda](https://www.anaconda.com/) and [pip](https://pypi.org/project/pip/) in the following steps: ```bash conda create -n splatam python=3.10 conda activate splatam conda install -c "nvidia/label/cuda-11.6.0" cuda-toolkit conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge pip install -r requirements.txt ``` <!-- Alternatively, we also provide a conda environment.yml file : ```bash conda env create -f environment.yml conda activate splatam ``` --> #### Windows For installation on Windows using Git bash, please refer to the [instructions shared in Issue#9](https://github.com/spla-tam/SplaTAM/issues/9#issuecomment-1848348403). #### Docker and Singularity Setup We also provide a docker image. We recommend using a venv to run the code inside a docker image: ```bash docker pull nkeetha/splatam:v1 bash bash_scripts/start_docker.bash cd /SplaTAM/ pip install virtualenv --user mkdir venv cd venv virtualenv --system-site-packages splatam source ./splatam/bin/activate pip install -r venv_requirements.txt ``` Setting up a singularity container is similar: ```bash cd </path/to/singularity/folder/> singularity pull splatam.sif docker://nkeetha/splatam:v1 singularity instance start --nv splatam.sif splatam singularity run --nv instance://splatam cd <path/to/SplaTAM/> pip install virtualenv --user mkdir venv cd venv virtualenv --system-site-packages splatam source ./splatam/bin/activate pip install -r venv_requirements.txt ``` ## Demo ### Online You can SplaTAM your own environment with an iPhone or LiDAR-equipped Apple device by downloading and using the <a href="https://apps.apple.com/au/app/nerfcapture/id6446518379">NeRFCapture</a> app. Make sure that your iPhone and PC are connected to the same WiFi network, and then run the following command: ```bash bash bash_scripts/online_demo.bash configs/iphone/online_demo.py ``` On the app, keep clicking send for successive frames. Once the capturing of frames is done, the app will disconnect from the PC and check out SplaTAM's interactive rendering of the reconstruction on your PC! Here are some cool example results: <p align="center"> <a href=""> <img src="./assets/collage.gif" alt="Logo" width="75%"> </a> </p> ### Offline You can also first capture the dataset and then run SplaTAM offline on the dataset with the following command: ```bash bash bash_scripts/nerfcapture.bash configs/iphone/nerfcapture.py ``` ### Dataset Collection If you would like to only capture your own iPhone dataset using the NeRFCapture app, please use the following command: ```bash bash bash_scripts/nerfcapture2dataset.bash configs/iphone/dataset.py ``` ## Usage We will use the iPhone dataset as an example to show how to use SplaTAM. The following steps are similar for other datasets. To run SplaTAM, please use the following command: ```bash python scripts/splatam.py configs/iphone/splatam.py ``` To visualize the final interactive SplaTAM reconstruction, please use the following command: ```bash python viz_scripts/final_recon.py configs/iphone/splatam.py ``` To visualize the SplaTAM reconstruction in an online fashion, please use the following command: ```bash python viz_scripts/online_recon.py configs/iphone/splatam.py ``` To export the splats to a .ply file, please use the following command: ```bash python scripts/export_ply.py configs/iphone/splatam.py ``` `PLY` format Splats can be visualized in viewers such as [SuperSplat](https://playcanvas.com/supersplat/editor) & [PolyCam](https://poly.cam/tools/gaussian-splatting). To run 3D Gaussian Splatting on the SplaTAM reconstruction, please use the following command: ```bash python scripts/post_splatam_opt.py configs/iphone/post_splatam_opt.py ``` To run 3D Gaussian Splatting on a dataset using ground truth poses, please use the following command: ```bash python scripts/gaussian_splatting.py configs/iphone/gaussian_splatting.py ``` ## Downloads DATAROOT is `./data` by default. Please change the `input_folder` path in the scene-specific config files if datasets are stored somewhere else on your machine. ### Replica Download the data as below, and the data is saved into the `./data/Replica` folder. Note that the Replica data is generated by the authors of iMAP (but hosted by the authors of NICE-SLAM). Please cite iMAP if you use the data. ```bash bash bash_scripts/download_replica.sh ``` ### TUM-RGBD ```bash bash bash_scripts/download_tum.sh ``` ### ScanNet Please follow the data downloading procedure on the [ScanNet](http://www.scan-net.org/) website, and extract color/depth frames from the `.sens` file using this [code](https://github.com/ScanNet/ScanNet/blob/master/SensReader/python/reader.py). <details> <summary>[Directory structure of ScanNet (click to expand)]</summary> ``` DATAROOT └── scannet └── scene0000_00 └── frames ├── color │ ├── 0.jpg │ ├── 1.jpg │ ├── ... │ └── ... ├── depth │ ├── 0.png │ ├── 1.png │ ├── ... │ └── ... ├── intrinsic └── pose ├── 0.txt ├── 1.txt ├── ... └── ... ``` </details> We use the following sequences: ``` scene0000_00 scene0059_00 scene0106_00 scene0181_00 scene0207_00 ``` ### ScanNet++ Please follow the data downloading and image undistortion procedure on the <a href="https://kaldir.vc.in.tum.de/scannetpp/">ScanNet++</a> website. Additionally for undistorting the DSLR depth images, we use our <a href="https://github.com/Nik-V9/scannetpp">own variant of the official ScanNet++ processing code</a>. We will open a pull request to the official ScanNet++ repository soon. We use the following sequences: ``` 8b5caf3398 b20a261fdf ``` For b20a261fdf, we use the first 360 frames, due to an abrupt jump/teleportation in the trajectory post frame 360. Please note that ScanNet++ was primarily intended as a NeRF Training & Novel View Synthesis dataset. ### Replica-V2 We use the Replica-V2 dataset from vMAP to evaluate novel view synthesis. Please download the pre-generated replica sequences from <a href="https://github.com/kxhit/vMAP">vMAP</a>. ## Benchmarking For running SplaTAM, we recommend using [weights and biases](https://wandb.ai/) for the logging. This can be turned on by setting the `wandb` flag to True in the configs file. Also make sure to specify the path `wandb_folder`. If you don't have a wandb account, first create one. Please make sure to change the `entity` config to your wandb account. Each scene has a config folder, where the `input_folder` and `output` paths need to be specified. Below, we show some example run commands for one scene from each dataset. After SLAM, the trajectory error will be evaluated along with the rendering metrics. The results will be saved to `./experiments` by default. ### Replica To run SplaTAM on the `room0` scene, run the following command: ```bash python scripts/splatam.py configs/replica/splatam.py ``` To run SplaTAM-S on the `room0` scene, run the following command: ```bash python scripts/splatam.py configs/replica/splatam_s.py ``` For other scenes, please modify the `configs/replica/splatam.py` file or use `configs/replica/replica.bash`. ### TUM-RGBD To run SplaTAM on the `freiburg1_desk` scene, run the following command: ```bash python scripts/splatam.py configs/tum/splatam.py ``` For other scenes, please modify the `configs/tum/splatam.py` file or use `configs/tum/tum.bash`. ### ScanNet To run SplaTAM on the `scene0000_00` scene, run the following command: ```bash python scripts/splatam.py configs/scannet/splatam.py ``` For other scenes, please modify the `configs/scannet/splatam.py` file or use `configs/scannet/scannet.bash`. ### ScanNet++ To run SplaTAM on the `8b5caf3398` scene, run the following command: ```bash python scripts/splatam.py configs/scannetpp/splatam.py ``` To run Novel View Synthesis on the `8b5caf3398` scene, run the following command: ```bash python scripts/eval_novel_view.py configs/scannetpp/eval_novel_view.py ``` For other scenes, please modify the `configs/scannetpp/splatam.py` file or use `configs/scannetpp/scannetpp.bash`. ### ReplicaV2 To run SplaTAM on the `room0` scene, run the following command: ```bash python scripts/splatam.py configs/replica_v2/splatam.py ``` To run Novel View Synthesis on the `room0` scene post SplaTAM, run the following command: ```bash python scripts/eval_novel_view.py configs/replica_v2/eval_novel_view.py ``` For other scenes, please modify the config files. ## Acknowledgement We thank the authors of the following repositories for their open-source code: - 3D Gaussians - [Dynamic 3D Gaussians](https://github.com/JonathonLuiten/Dynamic3DGaussians) - [3D Gaussian Splating](https://github.com/graphdeco-inria/gaussian-splatting) - Dataloaders - [GradSLAM & ConceptFusion](https://github.com/gradslam/gradslam/tree/conceptfusion) - Baselines - [Nice-SLAM](https://github.com/cvg/nice-slam) - [Point-SLAM](https://github.com/eriksandstroem/Point-SLAM) ## Citation If you find our paper and code useful, please cite us: ```bib @inproceedings{keetha2024splatam, title={SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM}, author={Keetha, Nikhil and Karhade, Jay and Jatavallabhula, Krishna Murthy and Yang, Gengshan and Scherer, Sebastian and Ramanan, Deva and Luiten, Jonathon}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, year={2024} } ``` ## Developers - [Nik-V9](https://github.com/Nik-V9) ([Nikhil Keetha](https://nik-v9.github.io/)) - [JayKarhade](https://github.com/JayKarhade) ([Jay Karhade](https://jaykarhade.github.io/)) - [JonathonLuiten](https://github.com/JonathonLuiten) ([Jonathan Luiten](https://www.vision.rwth-aachen.de/person/216/)) - [krrish94](https://github.com/krrish94) ([Krishna Murthy Jatavallabhula](https://krrish94.github.io/)) - [gengshan-y](https://github.com/gengshan-y) ([Gengshan Yang](https://gengshan-y.github.io/)) ", Assign "at most 3 tags" to the expected json: {"id":"5723","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"