US 11,694,333 B1 · Performing semantic segmentation of 3D data using deep learning

Abstract

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.

Claims20

Dependent claims

A deep artificial neural network for generating a semantically-segmented three-dimensional point cloud stored on a computer readable storage medium, wherein the deep artificial neural network is manufactured by a process comprising:

obtaining one or more training three-dimensional point clouds, wherein one or more features of the point clouds include a respective label, and wherein at least one of the features corresponds to an outdoor structure;
establishing a topology of the deep artificial neural network, wherein the network topology includes an input layer and an output layer having a predetermined number of neurons,
training the deep artificial neural network to output a set of point labels corresponding to an outdoor scene by, for each of the training three-dimensional point clouds:
subdividing the each point cloud into a plurality of subdivisions,
preprocessing the plurality of subdivisions, and
analyzing the features and respective labels of the training three-dimensional point clouds to update a set of weights of the deep artificial neural network;
and
storing the set of weights of the deep artificial neural network on the computer readable storage medium as parameters for initializing the deep artificial neural network.

5 more elements

A computer-implemented method for training a deep artificial neural network to generate a semantically-segmented three-dimensional point cloud, comprising:

obtaining one or more training three-dimensional point clouds, wherein one or more features of the point clouds include a respective label, and wherein at least one of the features corresponds to an outdoor structure;
establishing a topology of the deep artificial neural network, wherein the network topology includes an input layer and an output layer having a predetermined number of neurons;
training the deep artificial neural network to output a set of point labels corresponding to an outdoor scene by, for each of the training three-dimensional point clouds:
subdividing the each point cloud into a plurality of subdivisions,
preprocessing the plurality of subdivisions, and
analyzing the features and respective labels of the training three-dimensional point clouds to update a set of weights of the deep artificial neural network;
and
storing the set of weights of the deep artificial neural network as parameters for initializing the deep artificial neural network.

5 more elements

A server comprising:

one or more processors;
and a memory storing instructions that, when executed by the one or more processors, cause the server to
obtain one or more training three-dimensional point clouds, wherein one or more features of the point clouds include a respective label, and wherein at least one of the features corresponds to an outdoor structure;
establish a topology of the deep artificial neural network, wherein the network topology includes an input layer and an output layer having a predetermined number of neurons,
train the deep artificial neural network to output a set of point labels corresponding to an outdoor scene by, for each of the training three-dimensional point clouds:
subdividing the each point cloud into a plurality of subdivisions,
preprocessing the plurality of subdivisions, and
analyzing the features and respective labels of the training three-dimensional point clouds to update a set of weights of the deep artificial neural network;
and
store the set of weights of the deep artificial neural network on the memory as parameters for initializing the deep artificial neural network.

7 more elements

Dependencies are inferred from the claim text.

Performing semantic segmentation of 3D data using deep learning

Life of this patent

Performing semantic segmentation of 3D data using deep learning

Life of this patent

Abstract

Claims20