Virtual Point Cloud Reflectivity Estimation Using Deep Learning
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Solution Overview
Problem
Existing methods face challenges in accurately tagging object-related information from point cloud data generated by LIDAR sensors due to complexity, cost, and time, which hinder the development of deep learning algorithms, and virtual point cloud data lacks reflectivity information.
Innovation Solution
An electronic device that estimates reflectivity information of virtual point cloud data by acquiring image and point cloud data, detecting physical information including location and color information, and performing deep learning on the point cloud data to tag object-related information without using an actual LIDAR sensor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensor is used to collect point cloud data, then location information and reflectivity information can be acquired, but the complexity and cost of tagging object-related information increases significantly
Solution Approach 1:
The patent creates a virtual point cloud data model that copies the structural information from real LIDAR point cloud data without requiring actual LIDAR sensing. The virtual environment generates point cloud data through rendering techniques, producing a copy that preserves geometric structure while eliminating the need for complex real-world LIDAR tagging procedures.
Solution Approach 2:
The patent introduces an intermediary processing system that bridges image data and point cloud data. This intermediary system transfers reflectivity information from processed image data to virtual point cloud data through deep learning models, enabling reflectivity estimation without direct LIDAR measurement.
2Measurement precision
If additional sensor is used for accurate tagging, then object-related information accuracy improves, but the cost and time required for tagging increases
Solution Approach 1:
The patent performs preliminary processing of image data to extract reflectivity information before it is needed for point cloud tagging. By pre-processing images and extracting feature information in advance, the system prepares data that can be directly transferred to virtual point cloud models, eliminating the need for time-consuming real-time tagging operations.
Solution Approach 2:
The patent replaces the mechanical LIDAR sensing and manual tagging system with a computational approach using deep learning models. Instead of physically measuring reflectivity with sensors and manually annotating data, the system uses neural networks to automatically extract and transfer reflectivity information from images to point cloud data.
3Device complexity
If virtual point cloud data is used instead of real LIDAR data, then the complexity and cost of tagging is reduced, but reflectivity information is lost
Solution Approach 1:
The patent changes the parameter representation by transforming reflectivity information from direct LIDAR measurements into derived parameters through deep learning. The system learns to represent reflectivity as a function of image features and point cloud geometry, enabling reconstruction of reflectivity information from virtual point cloud data through parameter transformation rather than direct measurement.
Data Source
AI summary
An electronic device and an operational method thereof according to various embodiments relate to detection of reflectivity information of deep learning-based virtual environment point cloud data, and that is configured such that: image data and point cloud data are acquired; physical information including location information and color information for each point of point cloud data are detected on the basis of the image data; and deep learning for the point cloud data is performed on the basis of the physical information to detect reflectivity information of the point cloud data.