Apple Inc.

Shreyas V Joshi

Qi Shan

Aditya Sankar

Alexandre Da Veiga

Thorsten Gernoth

Onur Canturk Hamsici

Srikrishna Sridhar

Shawn R Scully

Carlos E Guestrin

Leon A Gatys

Gustav M Larsson

Amit Jain

Karla P Vega

Kory R Watson

Fareeha Irfan

David Guera Cobo

Bryan M Perfetti

Various implementations disclosed herein include devices, systems, and methods that determine a wrist measurement or watch band size using depth data captured by a depth sensor from one or more rotational orientations of the wrist. In some implementations, depth data captured by a depth sensor including at least two depth map images of a wrist from different angles is obtained. In some implementations, an output is generated based on inputting the depth data into a machine learning model, the output corresponding to circumference of the wrist or a watch band size of the wrist. Then, a watch band size recommendation is provided based on the output.

Touchless wrist measurement

A device implementing a system for providing predicted RGB images includes at least one processor configured to obtain an infrared image of a subject, and to obtain a reference RGB image of the subject. The at least one processor is further configured to provide the infrared image and the reference RGB image to a machine learning model, the machine learning model having been trained to output predicted RGB images of subjects based on infrared images and reference RGB images of the subjects. The at least one processor is further configured to provide a predicted RGB image of the subject based on output by the machine learning model.

Machine learning assisted image prediction

Various implementations disclosed herein include devices, systems, and methods that provide measurements of objects based on a location of a surface of the objects. An exemplary process may include obtaining a three-dimensional (3D) representation of a physical environment that was generated based on depth data and light intensity image data, generating a 3D bounding box corresponding to an object in the physical environment based on the 3D representation, determining a class of the object based on the 3D semantic data, determining a location of a surface of the object based on the class of the object, the location determined by identifying a plane within the 3D bounding box having semantics in the 3D semantic data satisfying surface criteria for the object, and providing a measurement of the object, the measurement of the object determined based on the location of the surface of the object.

Automatic measurements based on object classification

Various implementations disclosed herein include devices, systems, and methods that obtain a three-dimensional (3D) representation of a physical environment that was generated based on depth data and light intensity image data, generate a 3D bounding box corresponding to an object in the physical environment based on the 3D representation, classify the object based on the 3D bounding box and the 3D semantic data, and display a measurement of the object, where the measurement of the object is determined using one of a plurality of class-specific neural networks selected based on the classifying of the object.

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Seattle, WA, United States of America

Shreyas V Joshi