18497940. TECHNIQUES FOR RECONSTRUCTING DIFFERENT THREE-DIMENSIONAL SCENES USING THE SAME TRAINED MACHINE LEARNING MODEL simplified abstract (NVIDIA Corporation)

TECHNIQUES FOR RECONSTRUCTING DIFFERENT THREE-DIMENSIONAL SCENES USING THE SAME TRAINED MACHINE LEARNING MODEL

Organization Name

NVIDIA Corporation

Inventor(s)

Yang Fu of San Diego CA (US)

Sifei Liu of San Diego CA (US)

Jan Kautz of Lexington MA (US)

Xueting Li of Santa Clara CA (US)

Shalini De Mello of San Francisco CA (US)

Amey Kulkarni of San Jose CA (US)

Milind Naphade of Cupertino CA (US)

TECHNIQUES FOR RECONSTRUCTING DIFFERENT THREE-DIMENSIONAL SCENES USING THE SAME TRAINED MACHINE LEARNING MODEL - A simplified explanation of the abstract

This abstract first appeared for US patent application 18497940 titled 'TECHNIQUES FOR RECONSTRUCTING DIFFERENT THREE-DIMENSIONAL SCENES USING THE SAME TRAINED MACHINE LEARNING MODEL

Simplified Explanation

The scene reconstruction model described in the patent application generates 3D representations of scenes by mapping RGBD images associated with different viewpoints to surface representations and aggregating them in a 3D space.

• The model maps RGBD images to surface representations
• Aggregates surface representations in a 3D space
• Generates a fused surface representation of the scene
• Maps the fused surface representation to a 3D representation of the scene

Potential Applications

This technology could be applied in various fields such as virtual reality, augmented reality, gaming, robotics, and computer vision.

Problems Solved

This technology solves the problem of accurately reconstructing 3D scenes from RGBD images captured from different viewpoints.

Benefits

The benefits of this technology include improved scene reconstruction accuracy, enhanced visualization in virtual and augmented reality applications, and better object recognition in computer vision systems.

Potential Commercial Applications

Potential commercial applications of this technology include developing advanced virtual reality experiences, creating realistic gaming environments, improving robotic navigation systems, and enhancing object recognition in security systems.

Possible Prior Art

One possible prior art for this technology could be the use of RGBD images for scene reconstruction in computer vision research and applications.

How does this technology compare to existing scene reconstruction methods?

This article does not provide a direct comparison with existing scene reconstruction methods, so it is unclear how this technology stands out in terms of accuracy, efficiency, and performance.

What are the limitations of this technology in terms of scene complexity and scale?

The article does not address the potential limitations of this technology when dealing with highly complex scenes or large-scale environments, leaving room for further exploration and analysis.

Original Abstract Submitted

In various embodiments, a scene reconstruction model generates three-dimensional (3D) representations of scenes. The scene reconstruction model maps a first red, blue, green, and depth (RGBD) image associated with both a first scene and a first viewpoint to a first surface representation of at least a first portion of the first scene. The scene reconstruction model maps a second RGBD image associated with both the first scene and a second viewpoint to a second surface representation of at least a second portion of the first scene. The scene reconstruction model aggregates at least the first surface representation and the second surface representation in a 3D space to generate a first fused surface representation of the first scene. The scene reconstruction model maps the first fused surface representation of the first scene to a 3D representation of the first scene.