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Target reconstruction method based on dictionary learning

A dictionary learning and target technology, applied in the field of target reconstruction based on dictionary learning, can solve problems such as holes and noise, and achieve the effect of reducing the amount of calculation and calculation time

Active Publication Date: 2018-06-15
BEIHANG UNIV
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Problems solved by technology

[0005] The technology of the present invention solves the problem: overcomes the deficiencies of the prior art, and provides a method for object reconstruction based on dictionary learning to construct a sparse point cloud model of the target, and utilizes the sparse point cloud The local geometric similarity of the model is extended to avoid dense matching in the target non-textured area, and fundamentally solve the noise and hole problems caused by traditional methods

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  • Target reconstruction method based on dictionary learning
  • Target reconstruction method based on dictionary learning
  • Target reconstruction method based on dictionary learning

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Embodiment Construction

[0036] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0037] Such as figure 1 Shown, the present invention is a kind of target weight method based on dictionary learning, and concrete steps are as follows.

[0038] 1. Use the existing dense point cloud model to build a point cloud dictionary library

[0039] The elements in the point cloud patch library are taken from some existing 3D point cloud models. Specifically, record a 3D point cloud model as M={X 0 ,X 1 ,...,X t-1}, where X i is the points contained in the model M, t is the number of points contained in M, and M is divided into several point cloud patches, denoted as P 0 , P 1 ......P l-1 (l is the number of point cloud patches obtained by division), they satisfy formulas (7)-(9)

[0040] P 0 ∪P 1 ∪…P l-1 = M (7)

[0041]

[0042] the s min ≤|P i |≤s max (9)

[0043] Formula (7) and formula (9) show that P 0 , P 1 ......P ...

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Abstract

The invention relates to a target reconstruction method based on dictionary learning. Firstly, existing dense point cloud models are utilized for constructing a point cloud dictionary library; then asparse point cloud model of the target is constructed, and is expanded by virtue of the point cloud dictionary library for obtaining a complete dense three-dimensional model, and features are constructed in a process of expansion on the basis of local curvature invariance of point cloud surface pieces to be used as bases of expansion; and finally, surface reconstruction is carried out on the model, which is obtained by expansion in the previous step, to complete target reconstruction. The method can greatly reduce computation time, and has very good performance for reconstruction of targets ofwhich image texture is not rich or texture areas are repeated.

Description

technical field [0001] The invention relates to a target reconstruction method based on dictionary learning, which is suitable for targets with simple structure but lack of texture, can effectively solve holes and large-area missing in the reconstruction results of such targets, and improve the integrity of the reconstructed model. Background technique [0002] With the development of computer graphics and reverse engineering, people are paying more and more attention to how to obtain high-precision 3D models of objects. This technology is called 3D reconstruction technology. The 3D reconstruction technology mainly includes the steps of pre-model data acquisition and preprocessing, point cloud data registration and fusion, point cloud data surface reconstruction, etc., and finally converts the real object into a digital model that can be displayed by the computer. [0003] Similar to the human eyes that can perceive the three-dimensional information of space objects, three-d...

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Application Information

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IPC IPC(8): G06T17/00
CPCG06T17/00G06T2200/04G06T2207/20081
Inventor 袁丁刘韬张弘
Owner BEIHANG UNIV
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