3D Point Cloud Error Detection for Laser-Scan Mirror Artifacts
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Solution Overview
Problem
3D point clouds generated by laser scanners often contain erroneous mirror scanning points due to specularly reflective objects, which are manually corrected by identifying and deleting mirror scan points behind reflective surfaces.
Innovation Solution
A method and device that utilize a digital 3D model of the environment, either with specularly reflective surfaces or reflection values, to automatically identify and correct erroneous scanning points by checking if the line connecting the scan point to the laser origin intersects a reflective surface, and apply predefined criteria to confirm or correct these points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual correction of mirror scan points is performed, then accuracy of 3D point clouds is improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-processing the 3D point cloud data to identify and flag potential mirror scan points before the final correction step. The system calculates geometric relationships between scan points and reflective surfaces in advance, preparing identification results that guide subsequent automated correction, thereby reducing manual intervention time while maintaining accuracy
Solution Approach 2:
The patent introduces an intermediary computational layer that acts as a mediator between raw scan data and final corrected output. This intermediary system uses geometric algorithms to detect mirror artifacts and generates correction recommendations, serving as an automated assistant that bridges the gap between manual correction approaches and fully automated processing
2Object-affected harmful factors
If manual identification and deletion of mirror scan points is performed, then harmful mirror artifacts are removed, but operational complexity and user burden increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically identify, flag, and correct mirror scan points without requiring manual user intervention. The algorithm independently processes the 3D point cloud data, detects geometric inconsistencies caused by mirror reflections, and applies corrections autonomously, making the system self-sufficient and eliminating the need for manual operation
Solution Approach 2:
The patent replaces the manual mechanical process of identifying and deleting mirror points with an automated computational system. Instead of requiring users to visually inspect and manually remove artifacts, the system uses geometric algorithms and computational geometry to automatically detect and correct mirror artifacts, substituting human operation with automated mechanical-computational processes
3Reliability
If comprehensive verification of all scan points is performed, then reliability of 3D point cloud is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by focusing verification efforts only on specific regions of the 3D point cloud that are geometrically likely to contain mirror artifacts. Instead of uniformly processing all scan points, the system identifies areas near reflective surfaces and applies targeted verification algorithms to these local regions, maintaining high reliability where needed while preserving processing efficiency in other areas
Solution Approach 2:
The patent implements partial action by applying verification procedures selectively rather than comprehensively to all scan points. The system performs excessive verification only on scan points that exhibit geometric characteristics consistent with mirror artifacts, while using simpler verification or no verification for points that clearly do not exhibit mirror characteristics, thereby balancing reliability with processing efficiency
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Automatically detects and corrects erroneous scanning points, improving the accuracy of 3D point clouds by removing mirror artifacts and generating additional points from obscured areas, suitable for construction projects using Building Information Models (BIM).
Implementation Method 1
Distances to the scanning points are determined from time-of-flight measurements of the laser beams reflected from the scanning points in the environment
Implementation Method 2
If the environment contains specularly reflective objects, such as glass surfaces or reflective facade elements, erroneous mirror scanning points appear in the 3D point cloud
Data Source
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AI summary
The invention relates to methods and devices for error detection in 3D point clouds (PW) created with a laser scanner (1), based on a digital 3D model (M) of at least one surface (Ak) of an object (6) of the scanned environment (3), comprising, for each sampling point (pi) of the 3D point cloud (PW), whose connecting line (11) to the emission point (oi) of the laser measuring beam (2) that hit it intersects the said surface (Ak), recognizing this sampling point (pi) as being prone to errors.