Nvidia corporation (20240161383). TECHNIQUES FOR RECONSTRUCTING DIFFERENT THREE-DIMENSIONAL SCENES USING THE SAME TRAINED MACHINE LEARNING MODEL simplified abstract

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 20240161383 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 to create a fused surface representation of the scene. This fused representation is then mapped to a 3D representation of the scene.

• The model generates 3D representations of scenes by mapping RGBD images to surface representations.
• It aggregates these surface representations in a 3D space to create a fused representation of the scene.
• The fused representation is then converted into a 3D representation of the scene.

Potential Applications

The technology described in the patent application could have the following potential applications:

• Virtual reality and augmented reality content creation
• 3D modeling and visualization for architecture and design
• Robotics and autonomous navigation systems

Problems Solved

The technology addresses the following problems:

• Efficient generation of accurate 3D representations of scenes from RGBD images
• Seamless integration of multiple viewpoints into a single 3D representation
• Simplifying the process of creating 3D models of real-world environments

Benefits

The technology offers the following benefits:

• Improved accuracy and realism in 3D scene reconstructions
• Enhanced capabilities for virtual and augmented reality applications
• Streamlined workflow for creating 3D models from RGBD images

Potential Commercial Applications

The technology could be commercially applied in the following areas:

• Entertainment and gaming industries for immersive experiences
• Surveying and mapping industries for accurate 3D reconstructions
• Robotics and automation industries for navigation and object recognition

Possible Prior Art

One possible prior art for this technology could be the use of RGBD images for 3D reconstruction in computer vision research and applications.

How does the scene reconstruction model handle occlusions and overlapping surfaces in the scene representations?

The patent application does not provide specific details on how the model deals with occlusions and overlapping surfaces in the scene representations. Further information on the algorithms or techniques used for handling these challenges would be beneficial.

What computational resources are required to implement the scene reconstruction model, and how does it scale with larger scenes or higher resolutions?

The patent application does not mention the computational resources needed to implement the scene reconstruction model or how it scales with different scene sizes or resolutions. Understanding the computational requirements and scalability of the model would be essential for practical applications.

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.