20240087222.SPARSE VOXEL TRANSFORMER FOR CAMERA-BASED 3D SEMANTIC SCENE COMPLETION simplified abstract (nvidia corporation)

SPARSE VOXEL TRANSFORMER FOR CAMERA-BASED 3D SEMANTIC SCENE COMPLETION

Organization Name

nvidia corporation

Inventor(s)

Yiming Li of Jersey City NJ (US)

Zhiding Yu of Santa Clara CA (US)

Christopher B. Choy of Los Angeles CA (US)

Chaowei Xiao of Tempe AZ (US)

Jose Manuel Alvarez Lopez of Mountain View CA (US)

Sanja Fidler of Toronto (CA)

Animashree Anandkumar of Pasadena CA (US)

SPARSE VOXEL TRANSFORMER FOR CAMERA-BASED 3D SEMANTIC SCENE COMPLETION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240087222 titled 'SPARSE VOXEL TRANSFORMER FOR CAMERA-BASED 3D SEMANTIC SCENE COMPLETION

Simplified Explanation

An artificial intelligence framework is described that converts a two-dimensional image into three-dimensional semantic information using neural networks and transformers.

• Neural networks convert images into image feature maps, depth information, and query proposals.
• A first transformer processes the image feature maps using a cross-attention mechanism.
• The output of the first transformer, combined with a mask token, generates initial voxel features of the scene.
• A second transformer refines the initial voxel features using a self-attention mechanism.
• The refined voxel features are up-sampled and processed by a lightweight neural network to generate the three-dimensional semantic information.

1. 1. 1. Potential Applications

This technology can be used in autonomous vehicles for advanced driver assistance system (ADAS) functions.

1. 1. 1. Problems Solved

This technology solves the problem of converting two-dimensional images into three-dimensional semantic information for use in various applications.

1. 1. 1. Benefits

The benefits of this technology include improved depth perception and scene understanding for autonomous vehicles, leading to enhanced safety and performance.

1. 1. 1. Potential Commercial Applications

The commercial applications of this technology include integration into ADAS systems for automotive manufacturers.

1. 1. 1. Possible Prior Art

Prior art in this field may include research on neural networks and transformers for image processing and scene understanding.

1. 1. 1. 1. Unanswered Questions

1. 1. 1. 1. How does this technology compare to existing methods for converting 2D images into 3D semantic information?

This article does not provide a direct comparison with existing methods, so it is unclear how this technology differs or improves upon current approaches.

1. 1. 1. 1. What are the limitations of this technology in terms of processing speed and accuracy?

The article does not address the potential limitations of this technology in terms of processing speed and accuracy, leaving room for further investigation into these aspects.

Original Abstract Submitted

an artificial intelligence framework is described that incorporates a number of neural networks and a number of transformers for converting a two-dimensional image into three-dimensional semantic information. neural networks convert one or more images into a set of image feature maps, depth information associated with the one or more images, and query proposals based on the depth information. a first transformer implements a cross-attention mechanism to process the set of image feature maps in accordance with the query proposals. the output of the first transformer is combined with a mask token to generate initial voxel features of the scene. a second transformer implements a self-attention mechanism to convert the initial voxel features into refined voxel features, which are up-sampled and processed by a lightweight neural network to generate the three-dimensional semantic information, which may be used by, e.g., an autonomous vehicle for various advanced driver assistance system (adas) functions.