Abstract: A cross-range 3D object detection method and system operable for training a 3D object detection model with N sub-groups of a point cloud corresponding to N detection distance ranges to form N 3D object detection models forming an ensemble 3D object detection model. Training the 3D object detection model with the N sub-groups of the point cloud corresponding to the N detection distance ranges includes training the 3D object detection model progressively from distant to near. Training the 3D object detection model with the N sub-groups of the point cloud corresponding to the N detection distance ranges includes, each time the 3D object detection model converges, saving resulting weights and adding a corresponding network to the ensemble 3D object detection model.

Abstract: A method and apparatus for annotating point cloud data. An apparatus may be configured to cause display of the point cloud data, label points in the point cloud data with a plurality of annotation points, the plurality of annotation points corresponding to points on a human body, move, in response to a user input, one or more of the annotation points to define a human pose and create annotated point cloud data, and output the annotated point cloud data.

Abstract: A method and apparatus for annotating point cloud data. An apparatus may be configured to cause display of the point cloud data, label points in the point cloud data with a plurality of annotation points, the plurality of annotation points corresponding to points on a human body, move, in response to a user input, one or more of the annotation points to define a human pose and create annotated point cloud data, and output the annotated point cloud data.