Multiple dimensioned dense matching method and system

Abstract

The invention discloses a multiple dimensioned dense matching method and system. The multiple dimensioned dense matching method includes the following steps: constructing image pyramids for multiple images, and calculating a relevant matching image set of each image, wherein each image pyramid has a zeroth to (n-1)th layer and each layer corresponds to a dimension; constructing a depth range of each image; calculating a depth map, a normal vector map and a visibility map of each layer sequentially from the (n-1)th layer of each image pyramid, converting to the next layer of each image pyramid,and calculating the depth map, the normal vector map and the visibility map of the present layer by taking the depth map, the normal vector map and the visibility map of the previous layer of each image pyramid as initial values of an algorithm of the present layer until to the zeroth layer of each image pyramid; and carrying out depth map fusion according to the depth map, the normal vector mapand the visibility map of the zeroth layer of each image pyramid to generate point clouds. The method and system of the invention use a depth fusion framework to carry out dense matching, consider depth information, normal vector information and visibility information, and combines constrains of optical consistency and geometric consistency, thereby improving the precision. Meanwhile, a multiple dimensioned strategy is used, and so the memory requirements are reduced and the efficiency is improved.

Description

technical field [0001] The embodiments of the present application relate to the technical field of image processing, and in particular to a multi-scale dense matching method and system based on patch matching. Background technique [0002] Dense matching is one of the core techniques of image 3D reconstruction. When this technology is used, only image information is needed to realize the restoration of the three-dimensional point cloud information of the object. This technology has been widely used in surveying and mapping production, digital city modeling, virtual reality (Virtual Reality, VR), augmented reality (Augmented Reality, AR) and other fields. The point cloud generated by the dense matching technology can be compared with the laser point cloud, and has the characteristics of low price and high density. [0003] This technology is also widely used in many fields such as large-scale scene modeling and small object modeling. In the field of large scene reconstruct...

AI Technical Summary

Problems solved by technology

Commonly used algorithms include dense matching based on binocular vision and dense matching based on multi-vision. Among them, dense matching based on binocular vision is more efficient, but because it does not consider multiple images at the same time, its robustness and accuracy are relatively low. , such as the semi-global matching (SGM) algorithm based on the semi-global matching (SGM) algorithm; the dense matching algorithm based on multi-vision has a large amount of calculation, many algorithms are not suitable for parallelism, and require a large memory for operation, such as three-dimensional multi- Perspective stereo vision (patch-based multi-viewstereo, PMVS) algorithm

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