US 12,223,670 B2 · Using deep learning and structure-from-motion techniques to generate 3D point clouds from 2D data

Abstract

A server includes a processor and a memory storing instructions that, when executed by the processor, cause the server to receive two-dimensional (2D) images, analyze the images using a trained deep network to generate points, process the labeled points to identify tie points, and combine the 2D dimensional images into a three-dimensional (3D) point cloud using structure-from-motion. A method for generating a semantically-segmented 3D point cloud from 2D data includes receiving 2D images, analyzing the images using a trained deep network to generate labeled points, processing the points to identify tie points, and combining the 2D images into a 3D point cloud using structure-from-motion. A non-transitory computer readable storage medium stores executable instructions that, when executed by a processor, cause a computer to receive 2D images, analyze the images using a trained deep network to generate labeled points, process the points to identify and combine tie points using structure-from-motion.

Claims20

Dependent claims

A computer system comprising:

one or more processors;
and
a memory storing instructions that, when executed by the one or more processors, cause the computer system to:
receive, via one or more processors, at least one two-dimensional image corresponding to an outdoor scene, the outdoor scene including one or more outdoor objects;
and
analyze, via one or more processors, the at least one two-dimensional image using a trained deep artificial neural network to generate a respective set of one or more labeled points, each of the one or more labeled points corresponding to a respective class label describing at least one of outdoor objects depicted in the two-dimensional image, wherein the respective class label indicates a type of outdoor object corresponding to the labeled point.

3 more elements

Dependencies are inferred from the claim text.

Using deep learning and structure-from-motion techniques to generate 3D point clouds from 2D data

Life of this patent

Using deep learning and structure-from-motion techniques to generate 3D point clouds from 2D data

Life of this patent

Abstract

Claims20