Qualcomm incorporated (20240378872). LIDAR-CAMERA SPATIO-TEMPORAL ALIGNMENT USING NEURAL RADIANCE FIELDS simplified abstract
Contents
LIDAR-CAMERA SPATIO-TEMPORAL ALIGNMENT USING NEURAL RADIANCE FIELDS
Organization Name
Inventor(s)
Mireille Lucette Laure Gregoire of Stuttgart (DE)
Senthil Kumar Yogamani of Headford (IE)
LIDAR-CAMERA SPATIO-TEMPORAL ALIGNMENT USING NEURAL RADIANCE FIELDS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240378872 titled 'LIDAR-CAMERA SPATIO-TEMPORAL ALIGNMENT USING NEURAL RADIANCE FIELDS
Simplified Explanation: The patent application describes systems, methods, and devices for vehicle driving assistance systems that utilize image processing technology.
- The method involves receiving kinematic information from a camera image sensor and point cloud data from a LiDAR sensor.
- A neural radiance fields (NeRF) model is used to generate time-synchronized image data based on the received information.
- The processor then combines the image data and point cloud data to create fused data for enhanced driving assistance.
Key Features and Innovation:
- Integration of image processing technology in vehicle driving assistance systems.
- Utilization of NeRF model for generating time-synchronized image data.
- Fusion of image data and point cloud data for improved driving assistance capabilities.
Potential Applications: The technology can be applied in autonomous vehicles, advanced driver assistance systems (ADAS), and robotics for enhanced perception and decision-making capabilities.
Problems Solved: The technology addresses the need for accurate and real-time data fusion for effective driving assistance systems.
Benefits:
- Improved accuracy and reliability in driving assistance systems.
- Enhanced perception and decision-making capabilities for vehicles.
- Increased safety and efficiency on the roads.
Commercial Applications: The technology can be utilized by automotive manufacturers, technology companies, and research institutions for developing advanced driving assistance systems and autonomous vehicles.
Questions about Image Processing Technology: 1. How does the fusion of image data and point cloud data improve driving assistance systems? 2. What are the potential challenges in implementing image processing technology in vehicles?
Frequently Updated Research: Stay updated on advancements in image processing technology for driving assistance systems to ensure the latest innovations are incorporated into the technology.
Original Abstract Submitted
this disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. in a first aspect, a method of image processing includes receiving first kinematic information associated with a camera image sensor; receiving, by the processor, point cloud data from a light detection and ranging (lidar) sensor; generating, by the processor, first image data that is time-synchronized with the point cloud data based on the first kinematic information and a neural radiance fields (nerf) model; and generating, by the processor, fused data that combines the first image data and the point cloud data. other aspects and features are also claimed and described.
