CGal
colmap
CGal | colmap | |
---|---|---|
2 | 28 | |
4,558 | 6,794 | |
1.3% | 2.7% | |
10.0 | 9.2 | |
1 day ago | 3 days ago | |
C++ | C++ | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
CGal
-
The Orb: a parametric trackball with BTU mounted ball and keyboard switches for buttons
But I doubt any of this will ever run on the GPU... Multi-threading on the other hand, is already implemented and it works on the Gamma side, but I had to switch it off by default because CGAL doesn't seem to be there yet (see here for more). It does mostly work though, at least for the polyhedral operation which is what matters, although it may not be the great speed-up you expect it to be.
-
New to photogrammetry, getting started?
git clone https://github.com/CGAL/cgal.git
colmap
- Magic123: One Image to High-Quality 3D Object Generation
-
Drone mapping is pretty dang cool
Not saying its easy to use, but there is an application gui and it is free: https://github.com/colmap/colmap
-
Import many photogrammetry software's scenes into Blender
Colmap (Model folders (BIN and TXT), dense workspaces, NVM, PLY)
- Best options for monocular reconstruction?
-
improving camera pose estimation using multiple aruco markers
See colmap for example https://colmap.github.io/
-
2D images to 3D Object reconstruction
You're looking into a problem called photogrammetry, and a well-studied one at that. I'd recommend looking into "shape from motion" (sfm); specifically techniques that do "dense reconstruction." I'd recommend COLMAP to start with. It does pose estimation from images (e.g. you point it at a bunch of images and it will figure out the relative poses of the cameras that took them), as well as sparse and dense reconstcution.
-
Framework generate 3d meshes from camera images
COLMAP builds dense meshes from a collection of cameras https://colmap.github.io/
- Nerfstudio: A collaboration friendly studio for NeRFs
-
Neural Radiance Fields and input shape
I’ve seen references to using COLMAP (https://colmap.github.io/) to estimate camera position/pose, e.g. here
-
3D reconstruction of an object from videos/few images
Classical photogrammetry, where I agree with u/tdgros that the way to go is https://colmap.github.io/. There are actually better variants in literature but nothing is more reliable and user-friendly than COLMAP. This will give you a very precise point cloud, that can be meshed if needed.
What are some alternatives?
GLM - OpenGL Mathematics (GLM)
Meshroom - 3D Reconstruction Software
Eigen
OpenMVG (open Multiple View Geometry) - open Multiple View Geometry library. Basis for 3D computer vision and Structure from Motion.
ExprTK - C++ Mathematical Expression Parsing And Evaluation Library https://www.partow.net/programming/exprtk/index.html
Hierarchical-Localization - Visual localization made easy with hloc
Wykobi - Wykobi C++ Computational Geometry Library https://www.wykobi.com
nerf - Code release for NeRF (Neural Radiance Fields)
ceres-solver - A large scale non-linear optimization library
openMVS - open Multi-View Stereo reconstruction library
Boost.Multiprecision - Boost.Multiprecision
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more