Three-dimensional point cloud model reconstruction method, computer readable storage medium and device

Patent metadata

Field	Value
Publication number	US-10089781-B2
Application number	US-201615389959-A
Country	US
Kind code	B2
Filing date	Dec 23, 2016
Priority date	Dec 30, 2015
Publication date	Oct 2, 2018
Grant date	Oct 2, 2018

Abstract

The present disclosure provides a three-dimensional point cloud model reconstruction method and a device. The method comprises: 1) sampling and WLPO-consolidating an input point set to generate an initial surface point set, copying the initial surface point set as an initial position of an interior skeleton point set, to establish a correspondence relation between surface points and skeleton points; 2) moving points in the interior skeleton point set inwards along a direction opposite to a normal vector thereof, to generate interior points; 3) using a self-adaptive anisotropic neighborhood as a regularization term to perform an optimization of the interior points, and generating skeleton points; 4) performing a consolidation and completion of the initial surface point set using the skeleton points, to generate consolidated surface points; 5) reconstructing a three-dimensional point cloud model according to the skeleton points, the surface points and the correspondence relation between the surface points and the skeleton points.

First claim

The invention claimed is:

1. A three-dimensional point cloud model reconstruction method, comprising:
1) sampling and WLOP-consolidating an input point set to generate an initial surface point set, copying the initial surface point set as an initial position of an interior skeleton point set, to establish a correspondence relation between surface points and skeleton points;
2) moving points in the interior skeleton point set inwards along a direction opposite to a normal vector thereof, to generate interior points;
3) using a self-adaptive anisotropic neighborhood as a regularization term to perform an optimization of the interior points, and generating skeleton points;
4) performing a consolidation and completion of the initial surface point set using the skeleton points, to generate consolidated surface points;
5) reconstructing a three-dimensional point cloud model according to the skeleton points, the surface points and the correspondence relation between the surface points and the skeleton points;
wherein establishing a correspondence relation between surface points and skeleton points in step 1) comprises:
constructing a deep point set <P,Q>={<p i ,q i >} i∈I ⊂R 6 according to the surface point set and the skeleton point set, a deep point in the deep point set being composed of a point pair <p i ,q i >, wherein p i s a point in the surface point set P={p i } i∈I ⊂R 3 , q i is a point in the skeleton point set Q={q i } i∈I ⊂R 3 , and I is a sampled point set;
a direction of a deep point pair is m i =(p i −q i )/∥p i −q i ∥ and consistent with a normal vector of the surface point;
between step 1) and step 2), further comprising:
determining a size of a neighborhood of each point in the surface point set and the interior skeleton point set, wherein,
the neighborhood of the surface point is

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wherein a default value of σ p is 5;
the neighborhood of the interior skeleton point is

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between each sample point and an adjacent point; |P| is the number of the surface points; p i is a point in the surface point set, and p i′ is a surface point adjacent to p i .

2. The three-dimensional point cloud model reconstruction method according to claim 1 , wherein moving points in the interior skeleton point set inwards along a direction opposite to a normal vector thereof, to generate interior points in step 2) comprises:
determining a condition for stopping the inward movement of each sample point in the interior skeleton point set, wherein the condition for stopping the inward movement of the sample point is that in a neighborhood {tilde over (Q)} of the sample point, the maximum angle between the normal vector of sample point and those of neighboring points is smaller than a preset threshold

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Assignees

• Shenzhen Inst Of Adv Tech Cas

Inventors

• Huang Hui
• Wu Shihao
• Gong Minglun
• Zwicker Matthias
• Cohen-Or Daniel

Classifications

• G06T13/40

  of characters, e.g. humans, animals or virtual beings · CPC title

• G06T17/00 Primary

  Three-dimensional [3D] modelling for computer graphics · CPC title

• G06T7/20

  Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51) · CPC title

• G06T5/002

  Physics · mapped topic

• G06T7/60

  Analysis of geometric attributes · CPC title