US 12,307,738 B2
3D shape matching method and device based on 3D local feature description using SGHS
Dong Li, Guangdong (CN); Sheng Ao, Guangdong (CN); Jindong Tian, Guangdong (CN); and Yong Tian, Guangdong (CN)
Assigned to SHENZHEN UNIVERSITY, Guangdong (CN)
Appl. No. 17/042,550
Filed by SHENZHEN UNIVERSITY, Guangdong (CN)
PCT Filed Dec. 9, 2019, PCT No. PCT/CN2019/124038
§ 371(c)(1), (2) Date Oct. 3, 2022,
PCT Pub. No. WO2021/114027, PCT Pub. Date Jun. 17, 2021.
Prior Publication US 2023/0015645 A1, Jan. 19, 2023
Int. Cl. G06V 10/75 (2022.01); G06V 10/26 (2022.01); G06V 20/64 (2022.01)
CPC G06V 10/757 (2022.01) [G06V 10/26 (2022.01); G06V 20/64 (2022.01)] 14 Claims
OG exemplary drawing
 
1. A 3D shape matching method based on 3D local feature description using SGHs, comprising:
acquiring a 3D point cloud of a real scene;
acquiring a feature point p of the 3D point cloud of the real scene;
establishing a local reference frame for a spherical neighborhood of the feature point p, wherein an origin of the spherical neighborhood coincides with the feature point p and the spherical neighborhood has a support radius of R, and an origin of the local reference frame coincides with the feature point p and the local reference frame have an orthogonal and normalized x axis, y axis, and z axis;
establishing a 3D local feature descriptor based on the local reference frame to encode spatial information within the spherical neighborhood so as to acquire 3D local surface information within the spherical neighborhood; and
matching the 3D local surface information within the spherical neighborhood with 3D local surface information of a target object to perform 3D shape matching;
wherein the step of establishing the 3D local feature descriptor based on the local reference frame to encode spatial information within the spherical neighborhood comprises:
dividing the spherical neighborhood into a plurality of radial partitions along a radial direction with the origin of the spherical neighborhood as a center;
dividing the spherical neighborhood into a plurality of azimuth partitions with the z axis as a central axis;
dividing a first angle θz between a negative direction of the z axis and a positive direction of the z axis into a plurality of first deviation partitions with the origin of the spherical neighborhood as the center, where θz=π;
acquiring a 3D point set P within the spherical neighborhood, wherein P={pi|I=1, 2, 3, . . . , n}, pi is a neighborhood point within the spherical neighborhood, and n is the number of neighborhood points within the spherical neighborhood;
determining the radial partition where the neighborhood point pi is located, the azimuth partition where the neighborhood point pi is located, and the first deviation partition into which a first axial angle α between a normal vector ni of the neighborhood point pi and the z axis falls; and
generating a corresponding radial distribution histogram, a corresponding azimuth distribution histogram, and a corresponding first deviation distribution histogram respectively for the 3D point set P within the spherical neighborhood to characterize the 3D local surface information within the spherical neighborhood,
wherein the plurality of first deviation partitions of the spherical neighborhood are non-uniformly divided, and the first deviation angle is divided more densely where it is closer to the positive direction of the z axis, and
wherein the first angle θz is non-uniformly divided through establishing a sine function 2 sin (θ/2) about θ and dividing amplitude of the sine function non-uniformly, where θ∈}0, θz}.