Higher-order function networks for learning composable three-dimensional (3D) object and operating method thereof [patent]

Publication date

June 3, 2021

Inventors

Eric Mitchell, Selim Engin (Ph.D. student), Volkan Isler (professor), Daniel Lee

Abstract

An apparatus for representing a three-dimensional (3D) object, the apparatus includes a memory storing instructions, and a processor configured to execute the instructions to transmit a two-dimensional (2D) image to an external device, based on the 2D image being transmitted, receive, from the external device, mapping function parameters that are obtained using a first neural network, set a mapping function of a second neural network, based on the received mapping function parameters, and based on 3D samples, obtain the 3D object corresponding to the 2D image, using the second neural network of which the mapping function is set.

Link to patent application

Higher-order function networks for learning composable three-dimensional (3D) object and operating method thereof

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