Huawei technologies co., ltd. (20240282119). POINT CLOUD DATA PROCESSING METHOD, NEURAL NETWORK TRAINING METHOD, AND RELATED DEVICE simplified abstract
Contents
- 1 POINT CLOUD DATA PROCESSING METHOD, NEURAL NETWORK TRAINING METHOD, AND RELATED DEVICE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 POINT CLOUD DATA PROCESSING METHOD, NEURAL NETWORK TRAINING METHOD, AND RELATED DEVICE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Point Cloud Data Processing
- 1.13 Original Abstract Submitted
POINT CLOUD DATA PROCESSING METHOD, NEURAL NETWORK TRAINING METHOD, AND RELATED DEVICE
Organization Name
Inventor(s)
POINT CLOUD DATA PROCESSING METHOD, NEURAL NETWORK TRAINING METHOD, AND RELATED DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240282119 titled 'POINT CLOUD DATA PROCESSING METHOD, NEURAL NETWORK TRAINING METHOD, AND RELATED DEVICE
Simplified Explanation
The patent application describes a method for processing point cloud data using neural networks in the field of artificial intelligence.
Key Features and Innovation
- Obtaining point cloud data and dividing it into target cubes.
- Generating initial features for each target cube based on initial information of a target point.
- Updating initial features of target cubes using an attention mechanism.
- Performing feature processing on updated features to obtain a prediction result.
Potential Applications
This technology can be used in various fields such as autonomous driving, robotics, augmented reality, and environmental monitoring.
Problems Solved
This technology addresses the challenges of efficiently processing large amounts of point cloud data and extracting meaningful insights from it.
Benefits
- Improved accuracy in analyzing point cloud data.
- Enhanced efficiency in processing and interpreting complex data.
- Enables better decision-making based on predictive results.
Commercial Applications
- Autonomous driving systems for real-time environment analysis.
- Robotics for object recognition and navigation.
- Augmented reality applications for enhanced user experiences.
- Environmental monitoring for analyzing terrain and vegetation changes.
Prior Art
Readers can explore prior art related to point cloud data processing, neural networks, and artificial intelligence in academic journals, patent databases, and industry publications.
Frequently Updated Research
Stay updated on advancements in neural network training methods, point cloud data processing techniques, and applications of artificial intelligence in various industries.
Questions about Point Cloud Data Processing
What are the key benefits of using neural networks for processing point cloud data?
Neural networks offer enhanced accuracy and efficiency in analyzing complex point cloud data, leading to improved decision-making capabilities.
How can this technology impact the development of autonomous systems in different industries?
This technology can revolutionize autonomous systems by providing real-time analysis of the environment, enabling safer and more efficient operations.
Original Abstract Submitted
a point cloud data processing method, a neural network training method, and a related device are provided. the method may be applied to the field of point cloud data processing in the field of artificial intelligence. the method may include: obtaining point cloud data corresponding to a target environment, where the point cloud data is divided into a plurality of target cubes; generating an initial feature of each target cube based on initial information of a target point in each target cube; updating initial features of the plurality of target cubes based on an attention mechanism to obtain updated features of the plurality of target cubes; and performing a feature processing operation on the updated features of the plurality of target cubes to obtain a prediction result corresponding to the point cloud data.
