Augmenting 3D Point Clouds via Confidence-Based Merging
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Solution Overview
Problem
Existing augmented reality (AR) systems face challenges in accurately updating and enhancing previously captured 3D point clouds with data from subsequent captures, particularly when viewed from different perspectives or angles, leading to reduced accuracy and completeness of the AR data.
Innovation Solution
A method and system for augmenting preexisting AR data by correlating and merging 3D point clouds from initial and subsequent captures using techniques such as object matching, geolocation, and spatial orientation, allowing for the creation of a more accurate and comprehensive point cloud by combining confidence values and adding new points from subsequent captures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D point clouds from subsequent captures are merged with initial captures, then the completeness and accuracy of the point cloud is improved, but the complexity of data processing and correlation increases
Solution Approach 1:
The patent uses confidence values as an intermediary mechanism to facilitate the merging of point clouds from different captures. Each point is assigned a confidence value representing its reliability, and during merging, points are evaluated based on their confidence values relative to existing points. This intermediary metric simplifies the complex task of determining which points to keep, update, or discard when merging multiple point clouds, resolving the contradiction between improving accuracy and managing processing complexity.
2Loss of information
If multiple captures from different perspectives are integrated, then the completeness of the scene representation is improved, but the difficulty of correlating and aligning the data increases
Solution Approach 1:
The patent implements a feedback mechanism where confidence values are continuously updated based on comparisons between subsequent captures and the existing point cloud. When new points are added or existing points are updated, their confidence values are adjusted based on how well they align with and support the existing scene representation. This feedback loop enables automatic correlation and alignment of multi-perspective data, improving scene completeness while managing the complexity of data integration.
3Reliability
If confidence values are used to select and merge points, then the reliability of the augmented point cloud is improved, but the computational requirements increase
Solution Approach 1:
The patent changes the parameter of point selection from geometric criteria alone to a combined criteria including confidence values. Instead of relying solely on spatial relationships and geometric consistency, the system incorporates confidence values as an additional parameter that quantifies the reliability of each point. This parameter change enables more reliable point cloud augmentation by filtering out low-confidence points, while the computational overhead is managed by efficiently calculating and updating these confidence values during the merging process.
Data Source
AI summary
Methods for augmentation of previously captured augmented reality data with subsequently captured data are disclosed. A subsequent capture of a scene and associated AR data, such as a point cloud, may be correlated with the previously captured data by various methods, including image matching, object recognition, and geolocation referencing. Following correlation, the previously captured data may be augmented by the AR data from the subsequent capture, which may include adding or deleting points. Some existing points may be modified in some aspects where a subsequent capture includes more reliable data.