Nvidia corporation (20240331280). GENERATION OF 3D OBJECTS USING POINT CLOUDS AND TEXT simplified abstract

GENERATION OF 3D OBJECTS USING POINT CLOUDS AND TEXT

Organization Name

nvidia corporation

Inventor(s)

Yehonatan Kasten of Hinanit (IL)

Gal Chechik of Ramat Hasharon (IL)

GENERATION OF 3D OBJECTS USING POINT CLOUDS AND TEXT - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240331280 titled 'GENERATION OF 3D OBJECTS USING POINT CLOUDS AND TEXT

The patent application relates to using point clouds and text to generate 3D objects.

• Leveraging a pre-trained text-to-image diffusion model to reconstruct a complete 3D model of an object.
• Utilizing a sensor-captured incomplete point cloud and a textual description of the object.
• Representing the complete 3D model as a neural surface, polygonal mesh, radiance field, etc.
• Using a signed distance function (SDF) to measure the distance of 3D points from the nearest surface point.
• Enabling the use of incomplete point clouds to constrain surface location by encouraging the SDF to be zero in point cloud locations.

1. 1. 1. Potential Applications:

This technology can be applied in various industries such as manufacturing, architecture, virtual reality, and gaming for creating accurate 3D models from incomplete data.

1. 1. 1. Problems Solved:

This technology addresses the challenge of generating complete 3D models from incomplete point clouds and textual descriptions efficiently and accurately.

1. 1. 1. Benefits:

The benefits of this technology include improved accuracy in 3D model generation, reduced manual effort in modeling, and enhanced visualization capabilities.

1. 1. 1. Commercial Applications:

The technology can be commercialized in industries requiring 3D modeling, such as product design, animation, simulation, and virtual prototyping.

1. 1. 1. Prior Art:

Researchers can explore prior art related to text-to-image models, 3D reconstruction from point clouds, and neural surface representations for 3D objects.

1. 1. 1. Frequently Updated Research:

Stay updated on advancements in text-to-image models, point cloud processing algorithms, and neural network architectures for 3D reconstruction.

1. 1. 1. 1. Questions about 3D Object Generation:

1. How does the technology ensure accurate reconstruction of 3D objects from incomplete data?

  - The technology leverages a pre-trained text-to-image diffusion model and a signed distance function to reconstruct accurate 3D models.
  

2. What are the potential limitations of using point clouds and text for 3D object generation?

  - The limitations may include challenges in handling noisy point cloud data and accurately interpreting textual descriptions for complex objects.

Original Abstract Submitted

embodiments of the present disclosure relate to controlling generation of 3d objects using point clouds and text. systems and methods are disclosed that leverage a pre-trained text-to-image diffusion model to reconstruct a complete 3d model of an object from a sensor-captured incomplete point cloud for the object and a textual description of the object. the complete 3d model of the object may be represented as a neural surface (signed distance function), polygonal mesh, radiance field (neural surface and volumetric coloring function), and the like. the signed distance function (sdf) measures the distance of any 3d point from the nearest surface point, where positive or negative signs indicate that the point is outside or inside the object respectively. the sdf enables use of the incomplete point cloud for constraining the surface location by simply encouraging the signed distance function to be zero in the point cloud locations.