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US 2020/0005485 A1Published application
Three-dimensional bounding box from two-dimensional image and point cloud data
AssigneeZook, Inc.
InventorsDanfei Xu, Dragomir Dimitrov Anguelov, Ashesh Jain
Life of this patent
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Abstract
A three-dimensional bounding box is determined from a two-dimensional image and a point cloud. A feature vector associated with the image and a feature vector associated with the point cloud may be passed through a neural network to determine parameters of the three-dimensional bounding box. Feature vectors associated with each of the points in the point cloud may also be determined and considered to produce estimates of the three-dimensional bounding box on a per-point basis.
Claims21
(canceled)
A computer-implemented method comprising:
- receiving sensor data comprising a plurality of measurements of an environment;
- inputting at least a portion of the sensor data into a machine learned model;
- determining, as a first feature vector and based at least in part on a first portion of the machine learned model, a first set of values associated with a measurement of the plurality of measurements;
- determining, as a second feature vector and based at least in part on a second portion of the machine learned model, a second set of values associated with the plurality of measurements;
- combining, as a combined feature vector, the first feature vector and the second feature vector;
- inputting the combined feature vector into a third portion of the machine learned model;
- and
- receiving, from the third portion of the machine learned model, information associated with an object represented in the sensor data.
5 more elements
A system comprising:
- one or more processors;
- and
- non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the system to:
- input sensor data into a machine learned model, the sensor data including a plurality of measurements;
- determine, based on a first portion of the machine learned model, a first feature vector, the first feature comprising a first set of values associated with a first measurement of the plurality of measurements;
- determine, based on a second portion of the machine learned model, a second feature vector, the second feature vector comprising a second set of values associated with the plurality of measurements;
- combine the first feature vector and the second feature vector as a combined feature vector;
- input the combined feature vector into a third portion of the machine learned model;
- and
- receive, from the third portion of the machine learned model, information associated with an object represented in the sensor data.
7 more elements
A system comprising:
- an autonomous vehicle configured to operate in an environment;
- a sensor configured to generate point cloud data corresponding to the environment;
- one or more processors:
- and
- non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the system to perform acts comprising:
- inputting sensor data into a machine learned model, the sensor data including a plurality of measurements;
- determining, based on a first portion of the machine learned model, a first feature vector, the first feature comprising a first set of values associated with a first measurement of the plurality of measurements;
- determining, based on a second portion of the machine learned model, a second feature vector, the second feature vector comprising a second set of values associated with the plurality of measurements;
- combining the first feature vector and the second feature vector as a combined feature vector;
- inputting the combined feature vector into a third portion of the machine learned model;
- and
- receiving, from the third portion of the machine learned model, information associated with an object represented in the sensor data.
9 more elements
Dependencies are inferred from the claim text.