Abstract: A method of detecting objects in three-dimensional (3D) point clouds and detecting differences between 3D point clouds and the objects therein is disclosed. A method includes receiving a first scene 3D point cloud and a second scene 3D point cloud, wherein the first scene 3D point cloud and the second scene 3D point cloud include first and second target objects, respectively; aligning the first scene 3D point cloud and the second scene 3D point cloud; detecting the first and second target objects from the first scene 3D point cloud and the second scene 3D point cloud, respectively; comparing the detected first target object with the detected second target object; and identifying, based on the comparison, one or more differences between the detected first target object and the detected second target object. Further aspects relate to detecting changes of target objects within scenes of multiple 3D point clouds.

Abstract: A method of detecting objects in three-dimensional (3D) point clouds and detecting differences between 3D point clouds and the objects therein is disclosed. A method includes receiving a first scene 3D point cloud and a second scene 3D point cloud, wherein the first scene 3D point cloud and the second scene 3D point cloud include first and second target objects, respectively; aligning the first scene 3D point cloud and the second scene 3D point cloud; detecting the first and second target objects from the first scene 3D point cloud and the second scene 3D point cloud, respectively; comparing the detected first target object with the detected second target object; and identifying, based on the comparison, one or more differences between the detected first target object and the detected second target object. Further aspects relate to detecting changes of target objects within scenes of multiple 3D point clouds.

Abstract: A system and method of detecting one or more objects in a three-dimensional point cloud scene are provided. The method includes receiving a three-dimensional point cloud scene, the three-dimensional point cloud scene comprising a plurality of points; classifying at least a portion of the plurality of points in the three-dimensional point cloud into two or more categories by applying a classifying-oriented three-dimensional local descriptor and learning-based classifier; extracting from the three-dimensional point cloud scene one or more clusters of points utilizing the two or more categories by applying at least one of segmenting and clustering; and matching the extracted clusters with objects within a library by applying a matching-oriented three-dimensional local descriptor.

Abstract: A system and method of detecting one or more objects in a three-dimensional point cloud scene are provided. The method includes receiving a three-dimensional point cloud scene, the three-dimensional point cloud scene comprising a plurality of points; classifying at least a portion of the plurality of points in the three-dimensional point cloud into two or more categories by applying a classifying-oriented three-dimensional local descriptor and learning-based classifier; extracting from the three-dimensional point cloud scene one or more clusters of points utilizing the two or more categories by applying at least one of segmenting and clustering; and matching the extracted clusters with objects within a library by applying a matching-oriented three-dimensional local descriptor.