US Patent 11694333 Performing semantic segmentation of 3D data using deep learning

A deep artificial neural network (DNN) for generating a semantically-segmented three-dimensional (3D) point cloud is manufactured by a process including obtaining a 3D point cloud, establishing a DNN topology, training the DNN to output labels by subdividing the point cloud, pre-processing the subdivisions, updating weights, and storing weights. Training a DNN includes obtaining a 3D point cloud, establishing a topology of the DNN, training the DNN to output point labels by subdividing, pre-processing the subdivisions, analyzing the features and respective labels of the point cloud to update DNN weights, and storing the weights. A server includes a processor and a memory storing instructions that, when executed by the processor, cause the server to obtain a 3D point cloud, establish a DNN topology, train the DNN to output labels by subdividing, pre-process the subdivisions, analyze the features and respective labels of the point cloud to update weights, and store the weights.