As pertains to a large-scale point cloud free of limitations on the range and number of points as an object, the present invention labels the points that constitute the object irrespective of the type of the object. A three-dimensional point cloud label learning device 10A is provided with: a ground height calculation unit for accepting a three-dimensional point cloud and a ground height as inputs and outputting a three-dimensional point cloud with a ground height attached; an intensity-RGB conversion unit for accepting the three-dimensional point cloud with the ground height attached as an input and outputting a three-dimensional point cloud with an intensity-RGB converted ground height attached; a supervoxel clustering unit for accepting the three-dimensional point cloud with the intensity-RGB converted ground height attached, a point cloud label for learning, and a clustering hyperparameter as inputs and outputting supervoxel data with a correct answer label attached; and a deep neural network learning unit for accepting the supervoxel data with the correct answer label attached and a deep neural network hyperparameter as inputs and outputting a learned deep neural network parameter.
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