Nvidia corporation (20240161404). TECHNIQUES FOR TRAINING A MACHINE LEARNING MODEL TO RECONSTRUCT DIFFERENT THREE-DIMENSIONAL SCENES simplified abstract
Contents
TECHNIQUES FOR TRAINING A MACHINE LEARNING MODEL TO RECONSTRUCT DIFFERENT THREE-DIMENSIONAL SCENES
Organization Name
Inventor(s)
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 TRAINING A MACHINE LEARNING MODEL TO RECONSTRUCT DIFFERENT THREE-DIMENSIONAL SCENES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161404 titled 'TECHNIQUES FOR TRAINING A MACHINE LEARNING MODEL TO RECONSTRUCT DIFFERENT THREE-DIMENSIONAL SCENES
Simplified Explanation
The patent application describes a training application that uses machine learning to generate three-dimensional representations of two-dimensional images by mapping depth and viewpoint to signed distance function values and mapping RGB images to radiance values. The application then computes a reconstruction loss based on these values to modify pre-trained encoders and decoders, resulting in a trained model that generates 3D representations of RGBD images.
- Explanation:
- Training application uses machine learning to create 3D representations from 2D images
- Maps depth and viewpoint to signed distance function values
- Maps RGB images to radiance values
- Computes reconstruction loss to modify pre-trained encoders and decoders
- Generates trained model for 3D representation of RGBD images
- Potential Applications:**
- Virtual reality - Augmented reality - Gaming industry
- Problems Solved:**
- Converting 2D images to 3D representations - Enhancing visual quality in virtual environments
- Benefits:**
- Improved realism in virtual environments - Enhanced user experience in gaming and AR/VR applications
- Potential Commercial Applications:**
- Video game development - Architectural visualization - Medical imaging
- Possible Prior Art:**
- Prior art in machine learning for image-to-image translation - Prior art in 3D reconstruction from 2D images
- Unanswered Questions:**
- 1. How does the training application handle variations in lighting conditions when generating 3D representations from RGB images?**
The abstract does not provide specific details on how the training application accounts for different lighting conditions in the generation of 3D representations.
- 2. What is the computational complexity of the training process for modifying the pre-trained encoders and decoders based on the reconstruction loss?**
The abstract does not mention the computational resources or time required for the training application to modify the pre-trained models based on the reconstruction loss.
Original Abstract Submitted
in various embodiments, a training application trains a machine learning model to generate three-dimensional (3d) representations of two-dimensional images. the training application maps a depth image and a viewpoint to signed distance function (sdf) values associated with 3d query points. the training application maps a red, blue, and green (rgb) image to radiance values associated with the 3di query points. the training application computes a red, blue, green, and depth (rgbd) reconstruction loss based on at least the sdf values and the radiance values. the training application modifies at least one of a pre-trained geometry encoder, a pre-trained geometry decoder, an untrained texture encoder, or an untrained texture decoder based on the rgbd reconstruction loss to generate a trained machine learning model that generates 3d representations of rgbd images.
