University of Illinois

Seyedsohrab Madani

Haitham Al Hassanieh

Junfeng Guan

Saurabh Gupta

Suraj S Jog

A method includes receiving data including a plurality of data items, each data item of the plurality of data items including a three-dimensional (3D) radar heat map of an object and a corresponding two-dimensional (2D) image of the object captured by a stereo camera, inputting the training dataset into a machine learning model including a neural network (NN) that generates, from the 3D radar heat map, a 2D depth map for the object and outputs a probability that the 2D depth map is the corresponding 2D image of the object, and training the machine learning model based on a training dataset to generate a trained machine learning model that iteratively learns to generate an updated 2D depth map that approximates the corresponding 2D image. The training dataset includes the plurality of data items, the 2D depth map and the probability.

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Champaign, IL, United States of America

Seyedsohrab Madani