Apple inc. (20240331207). Encoding Three-Dimensional Data For Processing By Capsule Neural Networks simplified abstract
Contents
Encoding Three-Dimensional Data For Processing By Capsule Neural Networks
Organization Name
Inventor(s)
Nitish Srivastava of San Francisco CA (US)
Ruslan Salakhutdinov of Pittsburgh PA (US)
Hanlin Goh of Sunnyvale CA (US)
Encoding Three-Dimensional Data For Processing By Capsule Neural Networks - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240331207 titled 'Encoding Three-Dimensional Data For Processing By Capsule Neural Networks
The method described in the abstract involves receiving three-dimensional geometric elements as input, initializing geometric capsules with these elements, and updating the capsules based on the elements assigned to them. The method also includes a routing procedure that assigns additional geometric elements to the capsules based on surface correspondence and updates the feature component of each capsule.
- Three-dimensional geometric elements are received as input
- Geometric capsules are initialized with the elements
- Pose and feature components of the capsules are set with initial values
- Additional geometric elements are assigned to capsules based on surface correspondence
- Feature components of the capsules are updated based on the assigned elements
- The method outputs the geometric capsules with encoded three-dimensional data
Potential Applications: - Computer-aided design (CAD) - Virtual reality (VR) and augmented reality (AR) applications - Robotics and automation
Problems Solved: - Efficient representation and manipulation of complex geometric data - Improved accuracy in matching geometric elements to surfaces - Streamlined routing procedures for assigning elements to capsules
Benefits: - Enhanced visualization and manipulation of three-dimensional data - Increased efficiency in geometric modeling and analysis - Facilitates automation and optimization of geometric processes
Commercial Applications: Title: "Enhancing Geometric Modeling and Analysis with Three-Dimensional Data Encoding" This technology can be utilized in industries such as architecture, engineering, gaming, and medical imaging for improved visualization and analysis of complex geometric structures.
Prior Art: Researchers can explore prior art related to geometric modeling, surface correspondence algorithms, and three-dimensional data encoding techniques to understand the background of this technology.
Frequently Updated Research: Stay updated on advancements in geometric modeling algorithms, surface reconstruction methods, and data encoding techniques to enhance the capabilities of this technology.
Questions about Geometric Capsules: 1. How do geometric capsules improve the representation of three-dimensional data compared to traditional methods? 2. What are the key factors influencing the assignment of additional geometric elements to the capsules based on surface correspondence?
Original Abstract Submitted
a method includes receiving three-dimensional geometric elements as an input. the method also includes initializing geometric capsules by assigning one of the three-dimensional geometric elements to each of the geometric capsules and setting initial values for a pose component and a feature component of each of the geometric capsules. the method also includes one or more iterations of a routing procedure that includes assigning an additional one of the three-dimensional geometric elements to a respective one of the geometric capsules, based on correspondence of the additional one of the three-dimensional geometric elements to a surface defined based on the feature component of the respective one of the geometric capsules, and updating the feature component of each of the geometric capsules based on the three-dimensional geometric elements assigned to each of the geometric capsules. the method also includes outputting the geometric capsules including encoded three-dimensional data.
