MicMac vs OpenMVG

MicMac is free, open-source desktop software for reconstructing 3D models from imagery. Developed at IGN and ENSG within the LASTIG lab, it runs on Linux, macOS and Windows under the CECILL-B license. It supports frame cameras, pushbroom sensors and scanned analogue images, along with camera calibration models and dense image matching for deformation studies. Outputs can include point clouds, 3D models and ortho-imagery in a desired coordinate system. Intermediate results in open formats let users work at different stages, and quality indicators include bundle adjustment residuals, covariance matrices, parameter sensitivity and correlation images. The C3DC command computes a point cloud from oriented images; AperiCloud creates a sparse cloud with camera positions and orientations for viewing in MeshLab, but does not support 64-bit PLY. MicMac is adapted to big datasets and was initially built for experts and researchers, with use also described in architecture, archaeology and forestry. Documentation, tutorials, a wiki and user forum are available. Building from source involves multiple dependencies, and the Mac guide cautions that a current revision may not compile or may be unstable on a given OS.

OpenMVG is an open-source C++ library for multiple-view geometry, 3D computer vision, and structure from motion. It provides libraries, binaries, and pipelines for reconstructing 3D scenes from images. Library areas include image manipulation, feature description and matching, feature tracking, camera models, multiple-view geometry, robust estimation, and structure-from-motion algorithms. Its binaries can initialize scenes, detect and match features, perform reconstruction, and export scenes to other multiple-view-stereo frameworks. Those frameworks can compute dense point clouds or textured meshes from exported scenes. OpenMVG is released under the Mozilla Public License 2.0 and is listed as free. The project says it aims to support reproducible research through readable implementations of classic and state-of-the-art algorithms. It is designed for reading, learning, modification, and use, with test-driven development and samples. Tutorials cover data structures, library use, and processing an image dataset. Platforms listed are Windows, macOS, Linux, iOS, Android, and self-hosted, with desktop deployment, API access, point-cloud export, and ground control point support.