Space Coordinate Converting Server Using Marked Points for AR Alignment
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Solution Overview
Problem
Current technologies for Augmented and Mixed Reality (AR/MR) face inaccuracies in converting and aligning space coordinates, leading to errors in mapping virtual and actual environments.
Innovation Solution
A space coordinate converting server and method that receives a field video from an image capturing device, generates a point cloud model, determines key frames with rotation and translation information, maps points data to key images, and calculates 2D and 3D coordinates to establish a space coordinate converting relation, using techniques like SLAM and SVD for accurate coordinate alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current AR/MR technology is used to scan objects and generate virtual environment models, then virtual reality experience can be provided, but inaccuracies in space coordinate conversion and alignment occur
Solution Approach 1:
The patent introduces marked points as intermediary reference objects between the real world and virtual environment. These marked points serve as mediators to establish reliable correspondence between 2D image coordinates and 3D space coordinates, enabling accurate conversion and alignment through the key frame assembly that links multiple observations of these reference points
Solution Approach 2:
The system continuously tracks marked points across multiple frames and uses the accumulated position information to refine and correct coordinate conversions. The feedback mechanism allows the system to detect and compensate for drift and inaccuracies by repeatedly observing the same reference points and adjusting the spatial transformation parameters accordingly
2Measurement precision
If field video is processed to generate point cloud models and key frames, then coordinate conversion can be achieved, but processing time and computational complexity increase
Solution Approach 1:
The patent segments the video processing into discrete key frames separated by marked point detections. Instead of processing the entire video continuously, the system identifies and processes only these critical frames where marked points are detected, significantly reducing the computational workload while maintaining accurate coordinate conversion for the important temporal moments
Solution Approach 2:
The system pre-identifies and extracts marked point information from the video stream before performing the full coordinate conversion process. By preparing the reference point data in advance and organizing it into key frame assemblies, the system reduces the computational burden during the actual conversion operation
Data Source
AI summary
A space coordinate converting server and method thereof are provided. The space coordinate converting server receives a field video recorded with a 3D object from an image capturing device, and generates a point cloud model accordingly. The space coordinate converting server determines key frames of the field video, and maps the point cloud model to key images of the key frames based on rotation and translation information of the image capturing device for generating a characterized 3D coordinate set. The space coordinate converting server determines 2D coordinates of the 3D object in key images, and selects 3D coordinates from the characterized 3D coordinate set according to the 2D coordinates. The space coordinate converting server determines a space coordinate converting relation according to marked points of the 3D object and the 3D coordinates.