Three-dimensional reconstruction of machine vision

SFM (Structure from Motion) is a technology in the field of computer vision and computer graphics that is used to recover the structure and camera motion information of a three-dimensional scene from a series of images. SFM is commonly used in applications such as creating 3D models, scene reconstruction, navigation, pose estimation, etc.

The following is the general workflow and main steps of SFM:

1. Feature extraction and matching :

   • First, feature points are extracted from a series of input images, usually using feature detectors such as SIFT, SURF, and ORB.
   • These feature points are then matched to determine their correspondence in different images.

2. Camera positioning and pose estimation :

   • Initially, it is usually necessary to estimate the position and attitude of the camera. This can be achieved by feature point matching between the two images.
   • Once there are enough matching points, the position and pose of the camera can be estimated using methods such as RANSAC.

3. 3D point cloud reconstruction :

   • With the matched feature points, the position of the 3D point can be estimated using triangulation techniques.
   • Repeating this process gradually builds up a 3D point cloud representing the objects in the scene.

4. Keyframe selection :

   • Choose some keyframes as reference frames, usually where camera motion is obvious or a unique perspective in the scene.
   • For non-keyframes, camera pose estimation can be used to calculate their positions.

5. Map optimization :

   • Through methods such as Bundle Adjustment, camera parameters and the positions of three-dimensional points are globally optimized to reduce errors.
   • This helps improve the accuracy and consistency of reconstructions.

6. Dense reconstruction (optional):

   • Once you have a sparse 3D point cloud, you can use dense reconstruction algorithms, such as stereo matching, structure sensors, etc., to generate a denser point cloud or depth map.

7. Texture mapping and rendering :

   • If you need to generate a visual 3D reconstruction result, you can map the texture to the 3D model and render it.

8. Application fields :

   • The final SFM results can be used in different application fields, such as virtual reality, augmented reality, map production, autonomous navigation, object recognition, etc.

SFM is a complex field, and specific algorithms and implementations can vary depending on application scenarios and requirements. At the same time, modern SFM systems usually combine SLAM (Simultaneous Localization and Mapping) technology to estimate camera positions and build maps in real time. Various open source libraries and tools, such as OpenMVG, COLMAP, OpenCV, etc., provide tools and algorithms for SFM and SLAM.

OpenMVG (Open Multiple View Geometry) and COLMAP (Structure-from-Motion and Multi-View Stereo Computer Vision Toolkit) are both open source tools for image processing, 3D reconstruction and computer vision research. They both focus on Structure from Motion (SfM) and Multi-View Stereo (MVS) tasks, but differ in some aspects. Here is a brief introduction to them:

OpenMVG（Open Multiple View Geometry）：

1. Features :

   • OpenMVG is an open source SfM/MVS library for research and development.
   • It provides a set of tools for image processing, feature extraction, feature matching, 3D reconstruction, and camera pose estimation.
   • Various tasks of multi-view geometry (MVG) are supported, including image alignment, feature matching, 3D reconstruction, and point cloud generation.

2. Language :

   • OpenMVG is primarily written in C++, but a Python interface is also available.

3. Application :

   • OpenMVG is commonly used in research and development and can be used for building 3D models, scene reconstruction and multi-view tasks.
   • It supports not only SfM and MVS, but also other computer vision problems in multi-view tasks.

4. Visualization tools :

   • OpenMVG provides some visualization tools, but relatively few.

COLMAP (Structure-from-Motion and Multi-View Stereo Computer Vision Toolkit):

1. Features :

   • COLMAP is a feature-rich SfM/MVS software package designed for practical applications and research.
   • It provides a series of tools including functions such as SfM, MVS, image retrieval and 3D reconstruction.
   • COLMAP also includes functions for dense 3D reconstruction and camera calibration.

2. Language :

   • COLMAP is mainly written in C++, but also provides Python interfaces and GUI tools.

3. Application :

   • COLMAP is widely used in academic research and practical applications, including 3D modeling, map construction, virtual reality and augmented reality and other fields.
   • It has a user-friendly GUI interface and is suitable for users of different skill levels.

4. Visualization tools :

   • COLMAP provides powerful visualization tools for viewing reconstruction results, point clouds, and camera trajectories.

Choosing to use OpenMVG or COLMAP depends on your needs and skill level. If you need a full-featured, easy-to-use tool, COLMAP may be a good choice. If you are a researcher who wants more customization and control over the SfM/MVS process, OpenMVG may be more suitable for you. Whichever tool you choose, you can decide based on the requirements and complexity of your specific project.