18364783. CROSS-ATTENTION DECODING FOR VOLUMETRIC RENDERING simplified abstract (Toyota Jidosha Kabushiki Kaisha)

CROSS-ATTENTION DECODING 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)

CROSS-ATTENTION DECODING FOR VOLUMETRIC RENDERING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18364783 titled 'CROSS-ATTENTION DECODING FOR VOLUMETRIC RENDERING

Simplified Explanation

The patent application describes systems and methods that enhance computer vision capabilities, particularly for autonomous vehicle operation. The method involves generating a latent space and a decoder based on image data with multiple images of a scene from different viewing frames. A volumetric embedding is created to represent a novel viewing frame of the scene, and the decoder decodes the latent space using cross-attention with the volumetric embedding to generate a novel viewing frame of the scene.

• Explanation of the patent/innovation:

 * Generating a latent space and decoder based on image data with multiple images of a scene from different viewing frames.
 * Creating a volumetric embedding to represent a novel viewing frame of the scene.
 * Decoding the latent space using cross-attention with the volumetric embedding to generate a novel viewing frame of the scene.

1. 1. 1. Potential Applications:

The technology can be applied to autonomous vehicles, robotics, surveillance systems, and augmented reality devices.

1. 1. 1. Problems Solved:

This technology addresses the challenge of generating novel viewing frames of a scene from multiple images with different viewing angles.

1. 1. 1. Benefits:

The system enhances computer vision capabilities, improves scene reconstruction, and enables better understanding of complex environments.

1. 1. 1. Potential Commercial Applications:

The technology can be utilized in autonomous vehicle systems, robotics for navigation and mapping, surveillance systems for security monitoring, and augmented reality devices for immersive experiences.

1. 1. 1. Possible Prior Art:

One potential prior art could be research on image reconstruction and scene understanding using deep learning techniques in computer vision.

1. 1. 1. 1. Unanswered Questions:
         2. How does the system handle occlusions in the scene when generating novel viewing frames?

The patent application does not provide specific details on how occlusions in the scene are addressed during the generation of novel viewing frames.

1. 1. 1. 1. What computational resources are required to implement this system in real-time applications?

The patent application does not mention the computational resources needed to deploy this technology in real-time scenarios, which could be crucial for practical applications.

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 a latent space and a decoder based on image data that includes multiple images, where each image has a different viewing frame of a scene. The method also includes generating a volumetric embedding that is representative of a novel viewing frame of the scene. The method includes decoding, with the decoder, the latent space using cross-attention with the volumetric embedding, and generating a novel viewing frame of the scene based on an output of the decoder.