18364922. SHARED LATENT SPACES FOR VOLUMETRIC RENDERING simplified abstract (Toyota Jidosha Kabushiki Kaisha)

SHARED LATENT SPACES 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)

SHARED LATENT SPACES FOR VOLUMETRIC RENDERING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18364922 titled 'SHARED LATENT SPACES FOR VOLUMETRIC RENDERING

Simplified Explanation

The abstract describes a method for training a shared latent space and decoders based on image data to generate novel viewing frames of a scene.

• Training a shared latent space and decoders based on multiple images
• Generating a volumetric embedding for a novel viewing frame
• Decoding the shared latent space to generate the novel viewing frame

Potential Applications

This technology could be applied to various fields such as autonomous vehicle operation, surveillance systems, and augmented reality.

Problems Solved

This technology solves the problem of generating novel viewing frames of a scene from multiple images, which can be useful for tasks like object recognition, scene reconstruction, and navigation.

Benefits

The benefits of this technology include improved computer vision capabilities, enhanced object recognition, and the ability to generate novel views of a scene for various applications.

Potential Commercial Applications

Potential commercial applications of this technology include autonomous vehicles, security systems, virtual reality experiences, and robotics.

Possible Prior Art

One possible prior art could be research on generative models for image synthesis and computer vision tasks.

How does this method compare to existing techniques for generating novel viewing frames from multiple images?

This article does not provide a direct comparison to existing techniques for generating novel viewing frames, so it is unclear how this method differs or improves upon current methods.

What are the limitations of this technology in terms of scalability and real-time processing?

The article does not address the scalability or real-time processing capabilities of this technology, leaving questions about its practicality in large-scale applications or time-sensitive tasks.

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 An example method includes training a shared latent space and a first decoder based on first image data that includes multiple images, and training the shared latent space and a second decoder based on second image data that includes multiple images. The method also includes generating a volumetric embedding that is representative of a novel viewing frame the first scene. Further, the method includes decoding, with the first decoders, the shared latent space with the volumetric embedding, and generating the novel viewing frame of the first scene based on the output of the first decoder.