[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-72363":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":31,"readmeContent":32,"aiSummary":33,"trendingCount":16,"starSnapshotCount":16,"syncStatus":34,"lastSyncTime":35,"discoverSource":36},72363,"MASt3R-SLAM","rmurai0610\u002FMASt3R-SLAM","rmurai0610","[CVPR 2025] MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors","https:\u002F\u002Fedexheim.github.io\u002Fmast3r-slam\u002F",null,"Python",3057,363,30,55,0,4,11,60,12,29.68,"Other",false,"main",true,[27,28,29,30],"computer-vision","cvpr2025","robotics","slam","2026-06-12 02:03:02","[comment]: \u003C> (# MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors)\n\n\u003Cp align=\"center\">\n  \u003Ch1 align=\"center\">MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors\u003C\u002Fh1>\n  \u003Cp align=\"center\">\n    \u003Ca href=\"https:\u002F\u002Frmurai.co.uk\u002F\">\u003Cstrong>Riku Murai*\u003C\u002Fstrong>\u003C\u002Fa>\n    ·\n    \u003Ca href=\"https:\u002F\u002Fedexheim.github.io\u002F\">\u003Cstrong>Eric Dexheimer*\u003C\u002Fstrong>\u003C\u002Fa>\n    ·\n    \u003Ca href=\"https:\u002F\u002Fwww.doc.ic.ac.uk\u002F~ajd\u002F\">\u003Cstrong>Andrew J. Davison\u003C\u002Fstrong>\u003C\u002Fa>\n  \u003C\u002Fp>\n  \u003Cp align=\"center\">(* Equal Contribution)\u003C\u002Fp>\n\n[comment]: \u003C> (  \u003Ch2 align=\"center\">PAPER\u003C\u002Fh2>)\n  \u003Ch3 align=\"center\">\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2412.12392\">Paper\u003C\u002Fa> | \u003Ca href=\"https:\u002F\u002Fyoutu.be\u002Fwozt71NBFTQ\">Video\u003C\u002Fa> | \u003Ca href=\"https:\u002F\u002Fedexheim.github.io\u002Fmast3r-slam\u002F\">Project Page\u003C\u002Fa>\u003C\u002Fh3>\n  \u003Cdiv align=\"center\">\u003C\u002Fdiv>\n\n\u003Cp align=\"center\">\n    \u003Cimg src=\".\u002Fmedia\u002Fteaser.gif\" alt=\"teaser\" width=\"100%\">\n\u003C\u002Fp>\n\u003Cbr>\n\n# Getting Started\n## Installation\n```\nconda create -n mast3r-slam python=3.11\nconda activate mast3r-slam\n```\nCheck the system's CUDA version with nvcc\n```\nnvcc --version\n```\nInstall pytorch with **matching** CUDA version following:\n```\n# CUDA 11.8\nconda install pytorch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1  pytorch-cuda=11.8 -c pytorch -c nvidia\n# CUDA 12.1\nconda install pytorch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 pytorch-cuda=12.1 -c pytorch -c nvidia\n# CUDA 12.4\nconda install pytorch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 pytorch-cuda=12.4 -c pytorch -c nvidia\n```\n\nClone the repo and install the dependencies.\n```\ngit clone https:\u002F\u002Fgithub.com\u002Frmurai0610\u002FMASt3R-SLAM.git --recursive\ncd MASt3R-SLAM\u002F\n\n# if you've clone the repo without --recursive run\n# git submodule update --init --recursive\n\npip install -e thirdparty\u002Fmast3r\npip install -e thirdparty\u002Fin3d\npip install --no-build-isolation -e .\n \n\n# Optionally install torchcodec for faster mp4 loading\npip install torchcodec==0.1\n```\n\nSetup the checkpoints for MASt3R and retrieval.  The license for the checkpoints and more information on the datasets used is written [here](https:\u002F\u002Fgithub.com\u002Fnaver\u002Fmast3r\u002Fblob\u002Fmast3r_sfm\u002FCHECKPOINTS_NOTICE).\n```\nmkdir -p checkpoints\u002F\nwget https:\u002F\u002Fdownload.europe.naverlabs.com\u002FComputerVision\u002FMASt3R\u002FMASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth -P checkpoints\u002F\nwget https:\u002F\u002Fdownload.europe.naverlabs.com\u002FComputerVision\u002FMASt3R\u002FMASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth -P checkpoints\u002F\nwget https:\u002F\u002Fdownload.europe.naverlabs.com\u002FComputerVision\u002FMASt3R\u002FMASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_codebook.pkl -P checkpoints\u002F\n```\n\n## WSL Users\nWe have primarily tested on Ubuntu.  If you are using WSL, please checkout to the windows branch and follow the above installation.\n```\ngit checkout windows\n```\nThis disables multiprocessing which causes an issue with shared memory as discussed [here](https:\u002F\u002Fgithub.com\u002Frmurai0610\u002FMASt3R-SLAM\u002Fissues\u002F21).\n\n## Examples\n```\nbash .\u002Fscripts\u002Fdownload_tum.sh\npython main.py --dataset datasets\u002Ftum\u002Frgbd_dataset_freiburg1_room\u002F --config config\u002Fcalib.yaml\n```\n## Live Demo\nConnect a realsense camera to the PC and run\n```\npython main.py --dataset realsense --config config\u002Fbase.yaml\n```\n## Running on a video\nOur system can process either MP4 videos or folders containing RGB images.\n```\npython main.py --dataset \u003Cpath\u002Fto\u002Fvideo>.mp4 --config config\u002Fbase.yaml\npython main.py --dataset \u003Cpath\u002Fto\u002Ffolder> --config config\u002Fbase.yaml\n```\nIf the calibration parameters are known, you can specify them in intrinsics.yaml\n```\npython main.py --dataset \u003Cpath\u002Fto\u002Fvideo>.mp4 --config config\u002Fbase.yaml --calib config\u002Fintrinsics.yaml\npython main.py --dataset \u003Cpath\u002Fto\u002Ffolder> --config config\u002Fbase.yaml --calib config\u002Fintrinsics.yaml\n```\n\n## Downloading Dataset\n### TUM-RGBD Dataset\n```\nbash .\u002Fscripts\u002Fdownload_tum.sh\n```\n\n### 7-Scenes Dataset\n```\nbash .\u002Fscripts\u002Fdownload_7_scenes.sh\n```\n\n### EuRoC Dataset\n```\nbash .\u002Fscripts\u002Fdownload_euroc.sh\n```\n### ETH3D SLAM Dataset\n```\nbash .\u002Fscripts\u002Fdownload_eth3d.sh\n```\n\n## Running Evaluations\nAll evaluation script will run our system in a single-threaded, headless mode.\nWe can run evaluations with\u002Fwithout calibration:\n### TUM-RGBD Dataset\n```\nbash .\u002Fscripts\u002Feval_tum.sh \nbash .\u002Fscripts\u002Feval_tum.sh --no-calib\n```\n\n### 7-Scenes Dataset\n```\nbash .\u002Fscripts\u002Feval_7_scenes.sh \nbash .\u002Fscripts\u002Feval_7_scenes.sh --no-calib\n```\n\n### EuRoC Dataset\n```\nbash .\u002Fscripts\u002Feval_euroc.sh \nbash .\u002Fscripts\u002Feval_euroc.sh --no-calib\n```\n### ETH3D SLAM Dataset\n```\nbash .\u002Fscripts\u002Feval_eth3d.sh \n```\n\n## Reproducibility\nThere might be minor differences between the released version and the results in the paper after developing this multi-processing version. \nWe run all our experiments on an RTX 4090, and the performance may differ when running with a different GPU.\n\n## Acknowledgement\nWe sincerely thank the developers and contributors of the many open-source projects that our code is built upon.\n- [MASt3R](https:\u002F\u002Fgithub.com\u002Fnaver\u002Fmast3r)\n- [MASt3R-SfM](https:\u002F\u002Fgithub.com\u002Fnaver\u002Fmast3r\u002Ftree\u002Fmast3r_sfm)\n- [DROID-SLAM](https:\u002F\u002Fgithub.com\u002Fprinceton-vl\u002FDROID-SLAM)\n- [ModernGL](https:\u002F\u002Fgithub.com\u002Fmoderngl\u002Fmoderngl)\n\n# Citation\nIf you found this code\u002Fwork to be useful in your own research, please considering citing the following:\n\n```bibtex\n@inproceedings{murai2024_mast3rslam,\n  title={{MASt3R-SLAM}: Real-Time Dense {SLAM} with {3D} Reconstruction Priors},\n  author={Murai, Riku and Dexheimer, Eric and Davison, Andrew J.},\n  booktitle={Proceedings of the IEEE\u002FCVF Conference on Computer Vision and Pattern Recognition},\n  year={2025},\n}\n```\n","MASt3R-SLAM 是一个实时密集SLAM系统，结合了3D重建先验技术。该项目利用先进的计算机视觉算法和深度学习模型，在Python环境下实现了高精度的三维地图构建与定位功能。其核心技术包括基于ViT的大规模基础解码器以及高效的检索机制，能够显著提升SLAM系统的鲁棒性和准确性。特别适用于机器人导航、增强现实应用等需要精确环境感知与理解的场景中。此外，项目提供了详细的安装指南和支持多种CUDA版本的PyTorch环境配置说明，方便开发者快速上手使用。",2,"2026-06-11 03:41:30","high_star"]