With the rapid progress of modern computer technology, computer graphics, image processing, and aided design multimedia technology are more widely and deeply applied in advertising, games, medicine, film and television, and other fields. People often need to quickly obtain the three-dimensional information of the object’s surface and transform it into data that the computer can directly process. The three-dimensional reconstruction technology based on computer vision has an irreplaceable critical role. Three-dimensional reconstruction is a process of analyzing and calculating different properties of objects in three-dimensional space, such as color, texture, reflection, and other information, restoring the shape and color of objects through computer vision technology, and simulating rendering in an agreed way in the computer. It is a widely used technology, and its perception, modeling, and interaction with the real world involve computer vision, digital image processing, digital geometry processing, etc.
WiMi Hologram Cloud, Inc. (NASDAQ:WIMI) researches multi-view 3D reconstruction algorithms based on computer vision and artificial intelligence. The multi-view-based 3D reconstruction algorithm restores the object’s depth by calculating the 3D spatial position of the object image taken from different perspectives. It uses geometric constraints and feature-matching relations to find the corresponding feature-matching relation from the image to recover the spatial coordinate connection between the object and the camera and then carries out dense reconstruction to determine the position and orientation of each face. It integrates information from multiple images and has significant advantages in measuring three-dimensional objects and reconstructing highly realistic three-dimensional models. 3D reconstruction technology based on numerous views is increasingly applied in various fields, such as augmented reality, historical building modeling, etc. It has extensive application prospects in many areas.
The multi-view-based 3D reconstruction algorithm applied by WiMi mainly includes the following processes:
Feature point extraction and matching:
Feature point extraction and matching are essential modules in the process of reconstruction. Image feature point correspondence refers to the sparse pixel coordinate correspondence between images, a descriptor used to describe the content of a part of the image area. It is composed of multidimensional binary or real vectors and is generally obtained by extracting the gradient histogram of the part of the area. In computer vision, feature points are generally points with significant gradient changes on the image or the edges of objects in the picture. By matching feature points in the image, things can be recognized, or the scene’s location can be located.
The quality and accuracy of feature point extraction are particularly important, and the accuracy of feature point extraction will affect the quality of the corresponding position reconstruction model. The quality of feature points is mainly reflected in the accuracy of matching, distinguishing from different feature points, and easily matching features in the same position. However, there are apparent differences between other parts, so matching cannot be completed. In addition, it also needs invariance in various situations, such as rotation invariance, scale invariance, photometric invariance, and anti-interference ability to image noise, blur, image compression, and other factors.
Feature extraction and matching are the first steps in multi-view-based 3D reconstruction technology. In this process, the matching relationship between image pixels is established. Based on this step, the subsequent motion restoration structure algorithm can be carried out.
Motion recovery structure:
The process of motion restoration structure is mainly to extract the basic geometric information in 3D reconstruction, that is, to obtain the 3D position of the object seen and the pose of the camera from the 2D image sequence, and the focal length of the camera can be obtained from the incidental information in the picture or calibrated by itself. According to the theory of polar geometry, the relative position of the two cameras and the change in their orientation can be calculated through the feature-matching relation. Then, the feature points matched by the image can be triangulated to solve the depth of three-dimensional space points.
Multi-view stereo vision:
After the motion recovery structure, the pose of all cameras and the three-dimensional coordinates of objects recovered through image matching is obtained, namely, sparse point clouds. Multi-perspective stereo vision uses the information extracted from the motion recovery structure and the information in the under-utilized two-dimensional pictures to generate dense point clouds from sparse point clouds, making the news of the three-dimensional model more complete.
With the development of computer technology, 3D reconstruction technology has made significant progress and is usually used in many fields. WiMi’s multi-view-based 3D reconstruction algorithm has essential application value in navigation, virtual reality, augmented reality, mapping, medical treatment, and other areas. With the development of computer technology, 3D reconstruction technology has made significant progress and is usually used in many fields. WiMi’s multi-view-based 3D reconstruction algorithm has essential application value in navigation, virtual reality, augmented reality, mapping, medical treatment, and other areas.