Leveraging multidimensional sensor data for computationally efficient object detection for autonomous machine applications

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United States of America Patent

PATENT NO 11915145
APP PUB NO 20220383620A1
SERIAL NO

17883055

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Abstract

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In various examples, a two-dimensional (2D) and three-dimensional (3D) deep neural network (DNN) is implemented to fuse 2D and 3D object detection results for classifying objects. For example, regions of interest (ROIs) and/or bounding shapes corresponding thereto may be determined using one or more region proposal networks (RPNs)—such as an image-based RPN and/or a depth-based RPN. Each ROI may be extended into a frustum in 3D world-space, and a point cloud may be filtered to include only points from within the frustum. The remaining points may be voxelated to generate a volume in 3D world space, and the volume may be applied to a 3D DNN to generate one or more vectors. The one or more vectors, in addition to one or more additional vectors generated using a 2D DNN processing image data, may be applied to a classifier network to generate a classification for an object.

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Patent Owner(s)

  • NVIDIA CORPORATION

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Taneja, Rohit Fremont, US 12 31
Yoo, Innfarn Fremont, US 8 35

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