Toyota Research Institute, Inc.

Toyota Jidosha Kabushiki Kaisha

Mark Edward Tjersland

Junya Ueno

Kevin Stone

Yusuke Kanzawa

Thomas Kollar

Michael Laskey

A method for 3D object perception is described. The method includes extracting features from each image of a synthetic stereo pair of images. The method also includes generating a low-resolution disparity image based on the features extracted from each image of the synthetic stereo pair images. The method further includes predicting, by a trained neural network, a feature map based on the low-resolution disparity image and one of the synthetic stereo pair of images. The method also includes generating, by a perception prediction head, a perception prediction of a detected 3D object based on the feature map predicted by the trained neural network.

System and method for 3D object perception trained from pure synthetic stereo data

In one embodiment, a system for correcting parallax error is provided. An image is received from a camera and a plurality of points is received from a LiDAR sensor. The points are placed on the image based on coordinates associated with each point. The image is divided into a plurality of cells by placing a grid over the image. For each cell, a minimum distance between the points in the cell and the camera is determined. For each cell, a margin is calculated based on the determined minimum distance. For each cell, points that have a distance from the camera that is greater than the minimum distance plus the margin are removed or deleted. The image and/or the remaining points are then used to provide one or more vehicle functions.

Systems and methods for correcting parallax

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Mountain View, CA, United States of America

Mark Edward Tjersland