Three-dimensional point cloud label learning device, three-dimensional point cloud label estimating device, three-dimensional point cloud label learning method, three-dimensional point cloud label estimating method, and program

Abstract

A large-scale point cloud having no limitation on the range or the number of points is set as an object, and labels are attached to the points constituting the object regardless of the type of object. A three-dimensional point cloud label learning apparatusA includes a ground-based height calculation unit that receives a three-dimensional point cloud and a ground surface height as inputs and outputs a three-dimensional point cloud with a ground-based height, an intensity-RGB conversion unit that receives the three-dimensional point cloud with the ground-based height as an input and outputs an intensity-RGB converted three-dimensional point cloud with the ground-based height, a supervoxel clustering unit that receives the intensity-RGB converted three-dimensional point cloud with the ground-based height, a point cloud label for learning, and a clustering hyperparameter as inputs and outputs supervoxel data with a correct answer label, and a deep neural network learning unit that receives the supervoxel data with the correct answer label and a deep neural network hyperparameter as inputs and outputs a learned deep neural network parameter.