Patent Document ID: 10089781
Application ID: 15389959
Patent Status: 1

Claim One:
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 P ~ = { p i ′ |  p i ′ - p i  < σ p ⁢ r } , wherein a default value of σ p is 5; the neighborhood of the interior skeleton point is Q ~ i = { q i ′ |  q i ′ - q i  < σ q ⁢ r } , wherein a default value of σ q is 2; r is an average distance r = 1  P  ⁢ ∑ i ∈ I ⁢ min i ∈ I ⁢  ⁢ { i } ⁢  p i - p i ′  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 .