Quasi-dense matching extension method based on subspace fusion and consistency constraint

Abstract

The invention discloses a quasi-dense matching extension method based on subspace fusion and consistency constraint. The quasi-dense matching extension method comprises the following steps: firstly, acquiring reliable seed matching, and selecting a region to be extended around the seed matching; secondly, performing dense SIFT characteristic extraction on all pixel points to be extended in the region, and through subspace learning, fusing characteristic information and position information of the pixel points to be extended. During matching seeking, a local non-rigid transformation is learned through the consistency constraint, and compared with an affine transformation model and other models, the local non-rigid transformation can better describe nonplanar complex scenes; after each extension, an extension result is optimized to get rid of bad matching points; an extension process is repeated constantly until new matching cannot be found; the quasi-dense matching extension method has relatively good robustness for a scene with a complex surface structure; by the quasi-dense matching extension method, under the condition of less image, accurate and dense reconstruction of a target scene becomes possible.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and specifically relates to a quasi-dense matching extension method based on subspace fusion and consistency constraints. The method involves image matching, multi-view geometry, three-dimensional reconstruction, etc. The process of reconstructing an accurate and dense 3D point cloud of a target scene in the case of an image. Background technique [0002] The 3D reconstruction technology based on multi-view images has been widely used in autonomous navigation, cultural relics protection, 3D maps, virtual reality and other fields. The Structure from motion (SFM) algorithm is the current mainstream method. A typical SFM algorithm consists of the following four steps: First, use one or more cameras to capture multiple images of the target scene from different positions and perspectives. ; secondly, calculate the feature matching between all images, and construct a matching relationship grap...

AI Technical Summary

Problems solved by technology

[0006] The present invention aims to overcome the defects of the existing quasi-dense extension algorithm, and provides a quasi-dense matching extension method based on subspace fusion and consistency constraints,

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