Apple Inc.

Shreyas Saxena

Cuneyt Oncel Tuzel

Nitish Srivastava

Ricardo Da Silveira Cabral

Pavan Kumar Anasosalu Vasu

Dimitrios Kottas

Luciano Spinello

Wenda Wang

Guanhang Wu

A method includes determining a detection output that represents an object in a two-dimensional image using a detection model, wherein the detection output includes a shape definition that describes a shape and size of the object; defining a three-dimensional representation based on the shape definition, wherein the three-dimensional representation includes a three-dimensional model that represents the object that is placed in three-dimensional space according to a position and a rotation; determining a three-dimensional detection loss that describes a difference between the three-dimensional representation and three-dimensional sensor information; and updating the detection model based on the three-dimensional detection loss.

Object detection with position, pose, and shape estimation

A method includes obtaining training samples that include images that depict objects and annotations of annotated key point locations for the objects. The method also includes training a machine learning model to determine estimated key point locations for the objects and key point uncertainty values for the estimated key point locations by minimizing a loss function that is based in part on a key point localization loss value that represents a difference between the annotated key point locations and the estimated key point locations values and is weighted by the key point uncertainty values.

Key point recognition with uncertainty measurement

Growing community of inventors

Mountain View, CA, United States of America

Shreyas Saxena