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公开(公告)号：US12067737B2
公开(公告)日：2024-08-20
申请号：US18143524
申请日：2023-05-04
Applicant: Purdue Research Foundation
Inventor： Jie Shan , Xiangxi Tian
CPC classification number: G06T7/41 , G06T7/593 , G06T7/596 , G06V30/18143 , G06T2207/10021 , G06T2207/10028 , G06T2207/20021
Abstract: A method of determining macrotexture of an object is disclosed which includes obtaining a plurality of stereo images from an object by an imaging device, generating a coordinate system for each image of the plurality of stereo images, detecting one or more keypoints each having a coordinate in each image of the plurality of stereo images, wherein the coordinate system is based on a plurality of ground control points (GCPs) with apriori position knowledge of each of the plurality of GCPs, generating a sparse point cloud based on the one or more keypoints, reconstructing a 3D dense point cloud of the object based on the generated sparse point cloud and based on neighboring pixels of each of the one or more keypoints and calculating the coordinates of each pixel of the 3D dense point cloud, and obtaining the macrotexture based on the reconstructed 3D dense point cloud of the object.
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公开(公告)号：US11645769B2
公开(公告)日：2023-05-09
申请号：US17201051
申请日：2021-03-15
Applicant: Purdue Research Foundation
Inventor： Jie Shan , Xiangxi Tian
CPC classification number: G06T7/41 , G06T7/593 , G06T2207/10021 , G06T2207/10028 , G06T2207/20021
Abstract: A method of determining macrotexture of an object is presented which includes obtaining a plurality of stereo images from an object, generating a local coordinate system for each image, detecting one or more local keypoints each having a local coordinate, generating a global coordinate system based on a plurality of ground control points (GCPs) with apriori position knowledge of each of the plurality of GCPs, transforming the one or more local keypoints in each image to one or more global keypoints each having a global coordinate, generating a sparse point cloud based on the one or more global keypoints, reconstructing a 3D dense point cloud of the object based on neighboring pixels of each of the one or more local keypoints and calculating the global coordinates of each pixel of the 3D dense point cloud, and obtaining the macrotexture based on the reconstructed 3D dense point cloud of the object.
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公开(公告)号：US20230274450A1
公开(公告)日：2023-08-31
申请号：US18143524
申请日：2023-05-04
Applicant: Purdue Research Foundation
Inventor： Jie Shan , Xiangxi Tian
CPC classification number: G06T7/41 , G06T7/593 , G06T2207/20021 , G06T2207/10028 , G06T2207/10021
Abstract: A method of determining macrotexture of an object is disclosed which includes obtaining a plurality of stereo images from an object by an imaging device, generating a coordinate system for each image of the plurality of stereo images, detecting one or more keypoints each having a coordinate in each image of the plurality of stereo images, wherein the coordinate system is based on a plurality of ground control points (GCPs) with apriori position knowledge of each of the plurality of GCPs, generating a sparse point cloud based on the one or more keypoints, reconstructing a 3D dense point cloud of the object based on the generated sparse point cloud and based on neighboring pixels of each of the one or more keypoints and calculating the coordinates of each pixel of the 3D dense point cloud, and obtaining the macrotexture based on the reconstructed 3D dense point cloud of the object.
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公开(公告)号：US20210287383A1
公开(公告)日：2021-09-16
申请号：US17201051
申请日：2021-03-15
Applicant: Purdue Research Foundation
Inventor： Jie Shan , Xiangxi Tian
Abstract: A method of determining macrotexture of an object is presented which includes obtaining a plurality of stereo images from an object, generating a local coordinate system for each image, detecting one or more local keypoints each having a local coordinate, generating a global coordinate system based on a plurality of ground control points (GCPs) with apriori position knowledge of each of the plurality of GCPs, transforming the one or more local keypoints in each image to one or more global keypoints each having a global coordinate, generating a sparse point cloud based on the one or more global keypoints, reconstructing a 3D dense point cloud of the object based on neighboring pixels of each of the one or more local keypoints and calculating the global coordinates of each pixel of the 3D dense point cloud, and obtaining the macrotexture based on the reconstructed 3D dense point cloud of the object.
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