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

Toyota Jidosha Kabushiki Kaisha

Inventor(s)

Vitor Guizilini of Santa Clara CA (US)

Rares A. Ambrus of San Francisco CA (US)

Jiading Fang of Chicago IL (US)

Sergey Zakharov of San Francisco CA (US)

Vincent Sitzmann of Cambridge MA (US)

Igor Vasiljevic of Pacifica CA (US)

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

1. 1. 1. Unanswered Questions:
         1. 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.

1. 1. 1. 1. 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.