18179717. HAND SURFACE NORMAL ESTIMATION simplified abstract (Snap Inc.)
Contents
HAND SURFACE NORMAL ESTIMATION
Organization Name
Inventor(s)
HAND SURFACE NORMAL ESTIMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18179717 titled 'HAND SURFACE NORMAL ESTIMATION
Simplified Explanation: The patent application describes a system that uses hand surface normal estimation to augment images.
- 3D models of hands are generated from 3D hand data in various positions.
- Training data is created with normals of the 3D models and corresponding synthetic 2D image data.
- A normal estimation model is trained using the training data.
- The normal estimation is used in interactive applications to enhance hand image data.
Key Features and Innovation:
- Generation of 3D hand models from 3D hand data.
- Training of a normal estimation model using target normal training data and synthetic image training data.
- Application of normal estimation to augment hand image data.
Potential Applications:
- Virtual reality applications
- Gaming industry for realistic hand movements
- Medical field for hand surgery simulations
Problems Solved:
- Enhancing hand image data with surface normal estimation
- Improving realism in virtual environments
- Providing accurate hand augmentation in interactive applications
Benefits:
- Realistic augmentation of hand images
- Enhanced user experience in interactive applications
- Accurate representation of hand surfaces
Commercial Applications: Augmented reality devices, Virtual reality gaming, Medical simulation software
Prior Art: Prior art related to this technology may include research on hand surface normal estimation, 3D modeling of hands, and image augmentation techniques.
Frequently Updated Research: Research on improving the accuracy and efficiency of hand surface normal estimation algorithms.
Questions about Hand Surface Normal Estimation: 1. How does hand surface normal estimation improve image augmentation? 2. What are the potential challenges in implementing hand surface normal estimation in real-time applications?
Original Abstract Submitted
An system for augmenting images using hand surface normal estimation is provided. In a model training phase, 3D models of hands are generated using 3D data of hands in a variety of positions. Target normal training data is generated that includes normals of surfaces of the 3D models and synthetic 2D image training data corresponding to the 3D models and the normals. The target normal training data and the synthetic image training data are used to train a normal estimation model. The normal estimation is used by an interactive application to generate augmentations that are applied to hand image data.
