Method for calculating monocular video depth map

Abstract

The invention provides a method for calculating a monocular video depth map. The method is characterized by comprising a step of decomposing a video to be recovered into frames according to a frame, a step of extracting picture feature points of each frame, a step of matching the feature points and forming a feature point trajectory, a step of calculating a global rotation matrix and a translation vector, a step of optimizing camera parameters, a step of calculating dense optical flow of a selected frame, and a step of calculating a depth value of the selected frame to obtain a depth map. According to the method of the technical scheme, a depth estimation method of surface from motion (SFM) based on a physical mechanism is used, and a dense optical flow is used for matching. According to the method, no training sample is needed, optimization modes of segmentation, plane fitting and the like are not used, and the calculation quantity is small. At the same time, according to the method, a problem that the depth values of all pixels can not be obtained especially in a texture-free area in a process from sparse reconstruction to dense reconstruction in the prior art is solved, while the calculation efficiency is improved, the accuracy of the depth map is ensured.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to a calculation method for a monocular video depth map. Background technique [0002] With the development of science and technology, 3D movies and virtual reality are enriching people's lives. However, whether it is the 3D movies that have become popular all over the world or the virtual reality that is currently in the ascendant, there is a serious problem, that is, the lack of 3D resources at present. In the prior art, the depth is mainly predicted through monocular video, and binocular stereoscopic video is obtained through viewpoint synthesis, which is also the main method to solve the current shortage of 3D resources. [0003] In this technical approach, depth estimation of monocular video has received extensive attention as an important component. At present, the mainstream monocular depth prediction methods include depth estimation methods based on deep learning...

AI Technical Summary

Problems solved by technology

The method of the technical solution of the present invention, aiming at the situation that the existing supervised learning training samples are not easy to obtain, uses the method guaranteed by the physical mechanism to calculate the depth value of the monocular video; at the same time, it introduces dense optical flow for matching, which can be calculated more accurately Depth map of the video frame

Images