[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-11231":3},{"id":4,"name":5,"fullName":6,"owner":5,"repo":5,"description":7,"homepage":8,"htmlUrl":9,"language":10,"languages":8,"totalLinesOfCode":8,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":8,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":8,"rankLanguage":8,"license":8,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":21,"hasPages":21,"topics":23,"createdAt":8,"pushedAt":8,"updatedAt":29,"readmeContent":30,"aiSummary":31,"trendingCount":15,"starSnapshotCount":15,"syncStatus":32,"lastSyncTime":33,"discoverSource":34},11231,"colmap","colmap\u002Fcolmap","COLMAP - Structure-from-Motion and Multi-View Stereo",null,"https:\u002F\u002Fgithub.com\u002Fcolmap\u002Fcolmap","C++",11893,2031,173,647,0,35,76,244,105,44.92,false,"main",[24,25,26,27,28],"structure-from-motion","multi-view-stereo","reconstruction","geometry","computer-vision","2026-06-12 02:02:30","COLMAP\n======\n\nAbout\n-----\n\nCOLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo\n(MVS) pipeline with a graphical and command-line interface. It offers a wide\nrange of features for reconstruction of ordered and unordered image collections.\nThe software is licensed under the new BSD license.\n\nThe latest source code is available at https:\u002F\u002Fgithub.com\u002Fcolmap\u002Fcolmap. COLMAP\nbuilds on top of existing works and when using specific algorithms within\nCOLMAP, please also cite the original authors, as specified in the source code,\nand consider citing relevant third-party dependencies (most notably\nceres-solver, poselib, sift-gpu, vlfeat).\n\nDownload\n--------\n\n* Binaries for **Windows** and other resources can be downloaded\n  from https:\u002F\u002Fgithub.com\u002Fcolmap\u002Fcolmap\u002Freleases.\n* Binaries for **Linux\u002FUnix\u002FBSD** are available at\n  https:\u002F\u002Frepology.org\u002Fmetapackage\u002Fcolmap\u002Fversions.\n* Pre-built **Docker** images are available at\n  https:\u002F\u002Fhub.docker.com\u002Fr\u002Fcolmap\u002Fcolmap.\n* Conda packages are available at https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fcolmap and\n  can be installed with `conda install colmap`\n* **Python bindings** are available at https:\u002F\u002Fpypi.org\u002Fproject\u002Fpycolmap.\n  CUDA-enabled wheels are available at https:\u002F\u002Fpypi.org\u002Fproject\u002Fpycolmap-cuda12.\n* To **build from source**, please see https:\u002F\u002Fcolmap.github.io\u002Finstall.html.\n\nGetting Started\n---------------\n\n1. Download pre-built binaries or build from source.\n2. Download one of the provided [sample datasets](https:\u002F\u002Fdemuc.de\u002Fcolmap\u002Fdatasets\u002F)\n   or use your own images.\n3. Use the **automatic reconstruction** to easily build models\n   with a single click or command.\n\nDocumentation\n-------------\n\nThe documentation is available [here](https:\u002F\u002Fcolmap.github.io\u002F).\n\nTo build and update the documentation at the documentation website,\nfollow [these steps](https:\u002F\u002Fcolmap.github.io\u002Finstall.html#documentation).\n\nSupport\n-------\n\nPlease, use [GitHub Discussions](https:\u002F\u002Fgithub.com\u002Fcolmap\u002Fcolmap\u002Fdiscussions)\nfor questions and the [GitHub issue tracker](https:\u002F\u002Fgithub.com\u002Fcolmap\u002Fcolmap)\nfor bug reports, feature requests\u002Fadditions, etc.\n\nAcknowledgments\n---------------\n\nCOLMAP was originally written by [Johannes Schönberger](https:\u002F\u002Fdemuc.de\u002F) with\nfunding provided by his PhD advisors Jan-Michael Frahm and Marc Pollefeys.\nThe team of core project maintainers currently includes\n[Johannes Schönberger](https:\u002F\u002Fgithub.com\u002Fahojnnes),\n[Paul-Edouard Sarlin](https:\u002F\u002Fgithub.com\u002Fsarlinpe),\n[Shaohui Liu](https:\u002F\u002Fgithub.com\u002FB1ueber2y), and\n[Linfei Pan](https:\u002F\u002Flpanaf.github.io\u002F).\n\nThe Python bindings in PyCOLMAP were originally added by\n[Mihai Dusmanu](https:\u002F\u002Fgithub.com\u002Fmihaidusmanu),\n[Philipp Lindenberger](https:\u002F\u002Fgithub.com\u002FPhil26AT), and\n[Paul-Edouard Sarlin](https:\u002F\u002Fgithub.com\u002Fsarlinpe).\n\nThe project has also benefitted from countless community contributions, including\nbug fixes, improvements, new features, third-party tooling, and community\nsupport (special credits to [Torsten Sattler](https:\u002F\u002Ftsattler.github.io)).\n\nCitation\n--------\n\nIf you use this project for your research, please cite:\n\n    @inproceedings{schoenberger2016sfm,\n        author={Sch\\\"{o}nberger, Johannes Lutz and Frahm, Jan-Michael},\n        title={Structure-from-Motion Revisited},\n        booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},\n        year={2016},\n    }\n\n    @inproceedings{schoenberger2016mvs,\n        author={Sch\\\"{o}nberger, Johannes Lutz and Zheng, Enliang and Pollefeys, Marc and Frahm, Jan-Michael},\n        title={Pixelwise View Selection for Unstructured Multi-View Stereo},\n        booktitle={European Conference on Computer Vision (ECCV)},\n        year={2016},\n    }\n\nIf you use the global SfM pipeline (GLOMAP), please cite:\n\n    @inproceedings{pan2024glomap,\n        author={Pan, Linfei and Barath, Daniel and Pollefeys, Marc and Sch\\\"{o}nberger, Johannes Lutz},\n        title={{Global Structure-from-Motion Revisited}},\n        booktitle={European Conference on Computer Vision (ECCV)},\n        year={2024},\n    }\n\nIf you use the image retrieval \u002F vocabulary tree engine, please cite:\n\n    @inproceedings{schoenberger2016vote,\n        author={Sch\\\"{o}nberger, Johannes Lutz and Price, True and Sattler, Torsten and Frahm, Jan-Michael and Pollefeys, Marc},\n        title={A Vote-and-Verify Strategy for Fast Spatial Verification in Image Retrieval},\n        booktitle={Asian Conference on Computer Vision (ACCV)},\n        year={2016},\n    }\n\nContribution\n------------\n\nContributions (bug reports, bug fixes, improvements, etc.) are very welcome and\nshould be submitted in the form of new issues and\u002For pull requests on GitHub.\n\nLicense\n-------\n\nThe COLMAP library is licensed under the new BSD license. Note that this text\nrefers only to the license for COLMAP itself, independent of its thirdparty\ndependencies, which are separately licensed. Building COLMAP with these\ndependencies may affect the resulting COLMAP license.\n\n    Copyright (c), ETH Zurich and UNC Chapel Hill.\n    All rights reserved.\n\n    Redistribution and use in source and binary forms, with or without\n    modification, are permitted provided that the following conditions are met:\n\n        * Redistributions of source code must retain the above copyright\n          notice, this list of conditions and the following disclaimer.\n\n        * Redistributions in binary form must reproduce the above copyright\n          notice, this list of conditions and the following disclaimer in the\n          documentation and\u002For other materials provided with the distribution.\n\n        * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of\n          its contributors may be used to endorse or promote products derived\n          from this software without specific prior written permission.\n\n    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE\n    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n    POSSIBILITY OF SUCH DAMAGE.\n","COLMAP是一个用于运动恢复结构（SfM）和多视图立体视觉（MVS）的通用软件包，支持图形界面和命令行操作。它能够处理有序或无序图像集的重建任务，具备自动重建功能，用户只需单击或输入一条命令即可生成三维模型。该项目基于C++开发，并提供了Python接口以方便集成到其他应用中。COLMAP适用于需要从多张照片中恢复出三维场景信息的应用场景，如虚拟现实、增强现实、地理信息系统等领域。",2,"2026-06-11 03:31:29","trending"]