HIGHER-ORDER FUNCTION NETWORKS FOR LEARNING COMPOSABLE THREE-DIMENSIONAL (3D) OBJECT AND OPERATING METHOD THEREOF

HIGHER-ORDER FUNCTION NETWORKS FOR LEARNING COMPOSABLE THREE-DIMENSIONAL (3D) OBJECT AND OPERATING METHOD THEREOF

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 exter...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: ENGIN, Selim, MITCHELL, Eric, ISLER, Volkan, LEE, Daniel
Format: Patent
Sprache:eng ; fre ; ger
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
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Beschreibung
Zusammenfassung: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.