colmap

➡️To summarize, FlexGlue shows strong robustness on a wide range of applications, from SfM to satellite image matching. Thanks to @pesarlin, it is already available in COLMAP! colmap#4908 6/ t.co

COLMAP Tutorial: From Images to Accurate 3D Models 3D reconstruction is one of the critical approaches employed in photogrammetry, computer vision, robotics, and geospatial information system data processing. It makes it possible to reconstruct the shape of actual objects and scenes using a set of overlapping 2D images. COLMAP is an open-source structure-from-motion (SfM) and multi-view stereo (MVS) pipeline software for reconstruction of 3D models, camera pose, and dense point cloud from multiple images. COLMAP software is used extensively in archaeology, documentation of cultural heritage, architectural modeling, aerial mapping using drones, scientific research, and computer vision applications. What is COLMAP? COLMAP is an open-source photogrammetry and computer vision software for reconstructing 3D structures from sets of images. It has both a graphical user interface (GUI) and a command-line interface (CLI). Thus, users can use the tool in interactive mode or include it in automated workflows. COLMAP uses the following two techniques primarily: - Structure-from-Motion (SfM): This technique uses correspondences between features in multiple images to estimate cameras' positions and orientations as well as to reconstruct the sparse 3D structure of the scene. - Multi-View Stereo (MVS): With the help of camera parameters estimated with the SfM technique and multiple overlapping images, a denser reconstruction is computed. The outputs of the process include camera calibration, camera poses, sparse point clouds, dense point clouds, depth maps, and 3D reconstruction itself. Features of COLMAP Several features are supported by COLMAP for performing 3D reconstruction based on images. 1. Feature Detection and Matching COLMAP finds distinctive features in images and matches them from the overlapping images to provide correspondences among the cameras. 1. Camera Calibration and Pose Estimation CAMERA calibration is performed by COLMAP, where camera parameters, such as intrinsic camera parameters including focal lengths and distortion model parameters) and pose (extrinsic) camera parameters are estimated. Predefined calibration parameters can also be used when applicable. 1. Sparse 3D Reconstruction Sparse 3D reconstruction is done during the SfM process, where COLMAP constructs a 3D point cloud by triangulating corresponding features from the images. 1. Dense Point Cloud Creation By using the Multi-View Stereo technique, COLMAP determines depths for areas in images and fuses all these depths to create dense point clouds. 1. Graphical Interface and Command Line Interface GUI is used for interactive 3D reconstruction, while the CLI helps with a repeatable workflow and integration with other external programs for image processing. 1. Export/Import Reconstructed point clouds and camera models can be exported and imported from/to various file formats. Step-by-Step COLMAP Tutorial Step 1: COLMAP Installation Get a copy of COLMAP software from its official website or from the release page on GitHub. Choose a version compatible with your operating system. Official sources: Website: colmap.github.io Source code: colmap/colmap Complete the installation procedure according to your operating system instructions. If you use the GPU-enabled version, make sure the necessary drivers are installed. Upon completion, start the COLMAP GUI. Step 2: Arrange the Image Dataset Organize the image dataset in a project folder as follows: colmap_project/ ├── images/ │ ├── image_001.jpg │ ├── image_002.jpg │ ├── image_003.jpg │ └── image_004.jpg └── database.db Images are saved in the images folder, while the database db file contains the metadata about images, the extracted features, and the matching data. In the COLMAP GUI, create a new project and specify the database and images folders. Step 3: Feature Extraction from Images This process detects distinctive features and computes their descriptors that describe local appearances. This helps COLMAP identify the same locations in the various images. In the user interface, select the feature extraction process, set the camera model, and feature extraction parameters. When the images have been taken using the same camera and settings, the proper shared camera settings might be beneficial. The right setting will depend on whether the images were taken with the same physical camera, focal length, and image size. COLMAP will store these features in the project database. Step 4: Matching Features Between Images Matching of features determines the correspondence between keypoints of photographs. Such correspondences are necessary in establishing camera relationships and reconstructing scene geometry. COLMAP has several methods of matching that include: - Exhaustive matching: Matches all image pairs. This is helpful in smaller and unordered datasets. - Sequential matching: Prioritizes images that are close in the ordered dataset. Suitable for most video datasets or sequentially acquired images. - Spatial matching: Uses spatial relations to find potential pairs in the image when the location is known. - Vocabulary tree matching: Uses the method of image retrieval to find potential pairs in large datasets. Select a matching algorithm based on dataset properties and acquisition method. The post-matching process involves checking whether there are enough correspondences to establish a connection between the photographs. Step 5: Structure from Motion Reconstruction In this step, estimation of camera poses and a sparse 3D point cloud is performed. An incremental mapping approach is typically applied in COLMAP for image registration. Estimation of camera parameters, triangulation of corresponding points, and reconstruction optimization using bundle adjustment take place in this step. Bundle adjustment optimizes the parameters of cameras and 3D points' positions in order to minimize the reprojection error. The obtained sparse model consists of the following elements: - Camera poses - Camera calibration parameters - Sparse 3D points - Feature correspondences View the sparse reconstruction in the COLMAP viewer. A successful reconstruction normally has a well-connected camera network, reasonable geometry, and not many misregistered images. Some images may not register due to image overlap, texture, feature correspondence, and camera setup. Step 6: Creation of Dense Point Cloud Now that you have a good sparse reconstruction model, move on to dense reconstruction. COLMAP uses the computed poses and intrinsic parameters to calculate the depths from different views and combines the computed depths into a dense point cloud. In the GUI interface, click on the dense reconstruction process. It usually consists of three steps: image undistortion, stereo matching, and stereo fusion, depending on the COLMAP version. Computationally, dense reconstruction is more intensive than sparse reconstruction. It depends on the image resolution, number of images, and scene complexity. The result is a dense point cloud of the reconstructed scene. Step 7: Construct a Mesh or Texture 3D Model A point cloud is a description of a scene in terms of a set of 3D points. To produce a continuous surface for some application, a conversion of the point cloud to a polygon mesh may be carried out by means of the appropriate meshing tool. COLMAP is designed to perform SfM and MVS tasks, with its basic reconstruction pipeline revolving around these. Other tools may be used for surface reconstruction, mesh cleaning, generation of textures, and visualization, depending on the desired output. Some common post-processing actions may include: - Isolation of stray points and reconstruction artefacts - Outlier filtering - Surface reconstruction from the dense point cloud - Texture map generation or refinement - Mesh simplification for visualization - Output of the processed model into the proper format This will depend on whether one needs an architectural model, a digital model, a terrain, or visualization materials. Step 8: Export and Validate the Results Export the reconstruction in a format usable in the target application. COLMAP supports its own formats for reconstructions, and point clouds can be exported or converted to other formats using utilities. Some common downstream formats and applications include: - PLY: Point clouds and polygon meshes. - LAS/LAZ: Geospatial point cloud formats that need to be converted from the raw reconstruction. - OBJ: Polygon mesh and accompanying texture data. - GeoTIFF: Georeferenced raster products generated via a separate process chain. Validate your model before use in measurements and analyses. Look for any missing surfaces, discrepancies in geometry, noisy point clouds, and poor image registration. The COLMAP system is a highly versatile and free software suite that allows the user to reconstruct a 3D scene from images that overlap. It integrates Structure from Motion, camera pose estimation, bundle adjustment, and Multi-View Stereo to facilitate the creation of sparse and dense 3D reconstructions from photo sets. Through the process of creating quality images, proper choice of matching techniques, checking the quality of the reconstruction, as well as validating the reconstruction geometry, the user can create reliable 3D reconstructions for use in computer vision and photogrammetry. Nonetheless, the quality of the reconstruction will depend on factors such as the image set, camera settings, as well as whether there is reliable reference measurement data available. To learn more about COLMAP and its geospatial capabilities, click here. For more information or any questions regarding the LizardTech suite of products, please don't hesitate to contact us at: Email: [info@geowgs84.com](mailto:info@geowgs84.com) USA (HQ): (720) 702–4849 lizardtech.com (A GeoWGS84 Corp Company) lizardtech.com geowgs84.com

COLMAP: Structure-from-Motion and Multi-View Stereo (13.0k stars) What it does All text below is quoted directly from the project README. >COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. It offers a wide range of features for reconstruction of ordered and unordered image collections. The software is licensed under the new BSD license. >COLMAP builds on top of existing works and when using specific algorithms within COLMAP, please also cite the original authors, as specified in the source code, and consider citing relevant third-party dependencies (most notably ceres-solver, poselib, sift-gpu, vlfeat). Download: - Binaries for Windows and other resources can be downloaded from github.com/colmap/colmap/releases. - Binaries for Linux/Unix/BSD are available at repology.org. - Pre-built Docker images are available at hub.docker.com. - Conda packages are available at anaconda.org and can be installed with conda install colmap - Python bindings are available at pypi.org. CUDA-enabled wheels are available at pypi.org. AMD GPUs are supported via HIP/ROCm when building from source (see install docs). - To build from source, please see colmap.github.io. Getting Started: - Download pre-built binaries or build from source. - Download one of the provided sample datasets or use your own images. - Use the automatic reconstruction to easily build models with a single click or command. >COLMAP was originally written by Johannes Schönberger with funding provided by his PhD advisors Jan-Michael Frahm and Marc Pollefeys. The team of core project maintainers currently includes Johannes Schönberger, Paul-Edouard Sarlin, Shaohui Liu, and Linfei Pan. Repository colmap/colmap Repository stats |Metric|Value| |:-|:-| |Stars|12,955| |Forks|2,186| |Last release|4.2.1 (2026-09-29)| |Languages|C++ 86.7%, Python 7.7%, C 2.2%| |Contributors|193| |AI contributor accounts (Claude / Codex / Cursor / Copilot)|claude (5 commits)| |AI co-authored commits|Claude: 5| Main contributor |Field|Value| |:-|:-| |Name|Johannes Schönberger| |GitHub|@ahojnnes| |Commits to this repo|2,423 (top contributor)| |Bio|Engineer & Researcher| |Company / Location|Meta / Zurich| |Followers|3,327| |Public repos|82| |On GitHub since|2009-10-02|

COLMAP — From drone frames to camera positions and 3D points COLMAP — recovering camera positions and 3D points from overlapping images. ① Find features → ② match them across views → ③ estimate cameras and triangulate points → ④ refine the reconstruction. This run used 96 frames from a drone clip and recovered 69,316 sparse 3D points. We used its camera positions for our own 3D-object compositing and point-cloud fly-through effects. What place would you record as a point cloud? COLMAP은 겹치는 사진에서 카메라 위치와 3D 점을 함께 찾습니다. ① 특징점 찾기 → ② 사진끼리 매칭하기 → ③ 카메라 위치와 점 계산하기 → ④ 전체 결과 다듬기. 이번 실행은 드론 영상의 96개 프레임에서 희소 3D 점 69,316개를 찾았습니다. 계산된 카메라 위치를 활용해 저희가 3D 물체 합성과 점구름 비행 효과를 더했습니다. 어떤 장소를 점구름으로 기록해 보고 싶으세요? 📎 COLMAP · Johannes Schönberger et al. · © ETH Zurich and UNC Chapel Hill · new BSD github.com/colmap/colmap/tree/c4a3b308bddf391b4e7… github.com/colmap/colmap/blob/c4a3b308bddf391b4e7… Drone video: ProtoplasmaKid · CC0 · Wikimedia Commons commons.wikimedia.org Curated by @hyun2xyz #OpenSource #COLMAP #Photogrammetry #CreativeCoding