US Patent Application 17740584. METHOD, ELECTRONIC DEVICE, AND PROGRAM PRODUCT FOR TRAINING ENCODER AND PROCESSING DATA simplified abstract

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METHOD, ELECTRONIC DEVICE, AND PROGRAM PRODUCT FOR TRAINING ENCODER AND PROCESSING DATA

Organization Name

Dell Products L.P.


Inventor(s)

Wenbin Yang of Shanghai (CN)


Zijia Wang of WeiFang (CN)


Jiacheng Ni of Shanghai (CN)


Zhen Jia of Shanghai (CN)


METHOD, ELECTRONIC DEVICE, AND PROGRAM PRODUCT FOR TRAINING ENCODER AND PROCESSING DATA - A simplified explanation of the abstract

  • This abstract for appeared for US patent application number 17740584 Titled 'METHOD, ELECTRONIC DEVICE, AND PROGRAM PRODUCT FOR TRAINING ENCODER AND PROCESSING DATA'

Simplified Explanation

This abstract describes a method, electronic device, and program product for training an encoder and processing data. The method involves inputting sample point cloud data of an object into an encoder to obtain encoded data. The encoded data is then transformed to determine invariant portions (representing unchanging features) and variable portions (representing changing features) of the object. Based on these portions, a similarity loss and spatial loss are determined for the sample data. The parameter of the encoder is adjusted using these losses to obtain a trained encoder.


Original Abstract Submitted

Embodiments relate to a method, an electronic device, and a program product for training an encoder and processing data. The method includes inputting sample point cloud data for an object to an encoder to obtain encoded data for the object, and determining, by transforming the encoded data, a plurality of invariant portions for the object and a plurality of variable portions for the object, an invariant portion in the plurality of invariant portions indicating an invariant feature of the object and a variable portion in the plurality of variable portions indicating a variable feature of the object. The method further includes determining, based on the plurality of invariant portions and the plurality of variable portions, a similarity loss and a spatial loss for the sample point cloud data, and adjusting, based on the similarity loss and the spatial loss, a parameter of the encoder to obtain a trained encoder.

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