View source for 18364946. RADIANT AND VOLUMETRIC LATENT SPACE ENCODING FOR VOLUMETRIC RENDERING simplified abstract (Toyota Jidosha Kabushiki Kaisha)

=RADIANT AND VOLUMETRIC LATENT SPACE ENCODING FOR VOLUMETRIC RENDERING=


==Organization Name==

[[:Category:Toyota Jidosha Kabushiki Kaisha|Toyota Jidosha Kabushiki Kaisha]]


[[Category:Toyota Jidosha Kabushiki Kaisha]]

==Inventor(s)==

[[:Category:Vitor Guizilini of Santa Clara CA (US)|Vitor Guizilini of Santa Clara CA (US)]][[Category:Vitor Guizilini of Santa Clara CA (US)]]

[[:Category:Rares A. Ambrus of San Francisco CA (US)|Rares A. Ambrus of San Francisco CA (US)]][[Category:Rares A. Ambrus of San Francisco CA (US)]]

[[:Category:Jiading Fang of Chicago IL (US)|Jiading Fang of Chicago IL (US)]][[Category:Jiading Fang of Chicago IL (US)]]

[[:Category:Sergey Zakharov of San Francisco CA (US)|Sergey Zakharov of San Francisco CA (US)]][[Category:Sergey Zakharov of San Francisco CA (US)]]

[[:Category:Vincent Sitzmann of Cambridge MA (US)|Vincent Sitzmann of Cambridge MA (US)]][[Category:Vincent Sitzmann of Cambridge MA (US)]]

[[:Category:Igor Vasiljevic of Pacifica CA (US)|Igor Vasiljevic of Pacifica CA (US)]][[Category:Igor Vasiljevic of Pacifica CA (US)]]

[[:Category:Adrien Gaidon of San Jose CA (US)|Adrien Gaidon of San Jose CA (US)]][[Category:Adrien Gaidon of San Jose CA (US)]]

==RADIANT AND VOLUMETRIC LATENT SPACE ENCODING FOR VOLUMETRIC RENDERING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18364946 titled 'RADIANT AND VOLUMETRIC LATENT SPACE ENCODING FOR VOLUMETRIC RENDERING

==Simplified Explanation==

The patent application describes a method for enhancing computer vision capabilities, particularly for autonomous vehicle operation, by generating a shared latent space through training based on image data with multiple views of a scene and different types of embeddings. A decoder is trained based on one type of embeddings to generate an embedding representative of a novel viewing frame of the scene, which is then used to decode the shared latent space and generate the novel viewing frame of the scene.

* Explanation of the patent/innovation:
- Method for enhancing computer vision capabilities
- Training a shared latent space based on image data with multiple views and different types of embeddings
- Training a decoder to generate an embedding for a novel viewing frame of the scene
- Decoding the shared latent space using the generated embedding to generate the novel viewing frame of the scene

* Potential applications of this technology:
- Autonomous vehicle operation
- Surveillance systems
- Augmented reality applications

* Problems solved by this technology:
- Enhancing computer vision capabilities
- Generating novel viewing frames of a scene
- Improving accuracy and efficiency of image processing

* Benefits of this technology:
- Improved performance of autonomous vehicles
- Enhanced surveillance and security systems
- Enhanced user experience in augmented reality applications

* Potential commercial applications of this technology:
- Automotive industry for autonomous vehicles
- Security and surveillance industry
- Augmented reality technology companies

* Possible prior art:
- Prior methods of training computer vision models with image data and embeddings
- Prior techniques for generating novel viewing frames of a scene

### Unanswered Questions:
#### How does this method compare to existing computer vision techniques?
This article does not provide a direct comparison to existing computer vision techniques, leaving the reader to wonder about the specific advantages or disadvantages of this method compared to others.

#### What are the potential limitations or challenges of implementing this method in real-world applications?
The article does not address any potential limitations or challenges that may arise when implementing this method in real-world applications, leaving the reader curious about the practicality and feasibility of the technology.


==Original Abstract Submitted==

Systems and methods described herein support enhanced computer vision capabilities which may be applicable to, for example, autonomous vehicle operation. An example method includes generating, through training, a shared latent space based on (i) image data that include multiple images, where each image has a different viewing frame of a scene, and (ii) first and second types of embeddings, and training a decoder based on the first type of embeddings. The method also includes generating an embedding based on the first type of embeddings that is representative of a novel viewing frame of the scene, decoding, with the decoder, the shared latent space using cross-attention with the generated embedding, and generating the novel viewing frame of the scene based on an output of the decoder.



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