View source for 18549617. Machine-Learned Models for Implicit Object Representation simplified abstract (Google LLC)

=Machine-Learned Models for Implicit Object Representation=


==Organization Name==

[[:Category:Google LLC|Google LLC]]


[[Category:Google LLC]]

==Inventor(s)==

[[:Category:Cristian Sminchisescu of Zürich (CH)|Cristian Sminchisescu of Zürich (CH)]][[Category:Cristian Sminchisescu of Zürich (CH)]]

[[:Category:Thiemo Andreas Alldieck of Zürich (CH)|Thiemo Andreas Alldieck of Zürich (CH)]][[Category:Thiemo Andreas Alldieck of Zürich (CH)]]

[[:Category:Hongyi Xu of Zürich (CH)|Hongyi Xu of Zürich (CH)]][[Category:Hongyi Xu of Zürich (CH)]]

==Machine-Learned Models for Implicit Object Representation - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18549617 titled 'Machine-Learned Models for Implicit Object Representation

==Simplified Explanation==

The present disclosure describes a computer-implemented method for training a machine-learned model for implicit representation of an object, involving obtaining a latent code descriptive of the shape of an object, determining spatial query points, processing the latent code and spatial query points with segment representation portions of a machine-learned implicit object representation model, determining an implicit object representation of the object and semantic data, evaluating a loss function, and adjusting parameters of the model based on the loss function.

* Obtaining latent code descriptive of object shape
* Determining spatial query points
* Processing latent code and spatial query points with segment representation portions of a machine-learned model
* Determining implicit object representation and semantic data
* Evaluating loss function
* Adjusting model parameters based on loss function

== Potential Applications ==
This technology could be applied in various fields such as computer vision, robotics, and augmented reality for object recognition, segmentation, and manipulation tasks.

== Problems Solved ==
This technology helps in improving the accuracy and efficiency of object representation and segmentation in machine learning models.

== Benefits ==
The benefits of this technology include enhanced object representation, improved semantic understanding, and better performance in various applications.

== Potential Commercial Applications ==
Potential commercial applications of this technology include automated quality control in manufacturing, autonomous navigation systems, and virtual reality experiences.

== Possible Prior Art ==
One possible prior art could be the use of convolutional neural networks for object recognition and segmentation tasks in computer vision.

=== What are the limitations of the proposed method in terms of scalability and real-time processing? ===

The proposed method may face challenges in scaling up to handle large datasets and processing requirements in real-time applications due to the computational complexity involved in training and inference.

=== How does the proposed method compare to existing techniques in terms of accuracy and generalization to unseen data? ===

The proposed method may need to be benchmarked against existing techniques to evaluate its accuracy and generalization capabilities, especially when dealing with unseen data and complex object shapes.


==Original Abstract Submitted==

Systems and methods of the present disclosure are directed to a computer-implemented method for training a machine-learned model for implicit representation of an object. The method can include obtaining a latent code descriptive of a shape of an object comprising one or more object segments. The method can include determining spatial query points. The method can include processing the latent code and spatial query points with segment representation portions of a machine-learned implicit object representation model to obtain implicit segment representations for the object segments. The method can include determining an implicit object representation of the object and semantic data. The method can include evaluating a loss function. The method can include adjusting parameters of the machine-learned implicit object representation model based at least in part on the loss function.



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