Zoox, Inc.

Arsalan Mousavian

Dragomir Dimitrov Anguelov

John Patrick Flynn

James William Vaisey Philbin

A three dimensional bounding box is determined from a two dimensional image. A two dimensional bounding box is calculated based on a detected object within the image. A three dimensional bounding box is parameterized as having a yaw angle, dimensions, and a position. The yaw angle is defined as the angle between a ray passing through a center of the two dimensional bounding box and an orientation of the three dimensional bounding box. The yaw angle and dimensions are determined by passing the portion of the image within the two dimensional bounding box through a trained convolutional neural network. The three dimensional bounding box is then positioned such that the projection of the three dimensional bounding box into the image aligns with the two dimensional bounding box previously detected. Characteristics of the three dimensional bounding box are then communicated to an autonomous system for collision and obstacle avoidance.

Three dimensional bounding box estimation from two dimensional images

Systems, devices, and methods are described for generating dense depth estimates, and confidence values associated with such depth estimates, from image data. A machine learning algorithm can be trained using image data and associated depth values captured by one or more LIDAR sensors providing a ground truth. When the algorithm is deployed in a machine vision system, image data and/or depth data can be used to determine dense depth estimates for all pixels of the image data, as well as confidence values for each depth estimate. Such confidence values may be indicative of how confident the machine learned algorithm is of the associated depth estimate.

Growing community of inventors

Fairfax Station, VA, United States of America

Arsalan Mousavian