SELF-SUPERVISED THREE-DIMENSIONAL LOCATION PREDICTION USING MACHINE LEARNING MODELS

Certain aspects of the present disclosure provide techniques method for self-supervised training of a machine learning model to predict the location of a device in a spatial environment, such as a spatial environment including multiple discrete planes. An example method generally includes receiving...

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Bibliographic Details
Main Authors KARMANOV, Ilia, DIJKMAN, Daniel Hendricus Franciscus, PORIKLI, Fatih Murat, ACKERMANN, Hanno, GHAZVINIAN ZANJANI, Farhad
Format Patent
LanguageEnglish
Published 18.05.2023
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:Certain aspects of the present disclosure provide techniques method for self-supervised training of a machine learning model to predict the location of a device in a spatial environment, such as a spatial environment including multiple discrete planes. An example method generally includes receiving an input data set of scene data. A generator model is trained to map scene data in the input data set to points in three-dimensional space. One or more critic models are trained to backpropagate a gradient to the generator model to push the points in the three-dimensional space to one of a plurality of planes in the three-dimensional space. At least the generator is deployed.
Bibliography:Application Number: US202218047796