3D Point Cloud Surface Feature Identification via Path Segmentation
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Solution Overview
Problem
Conventional machine vision techniques face inefficiencies when processing 3D point cloud data, which includes massive numbers of 3D points with limited information on spatial relationships, making it challenging to identify object features in a timely and resource-efficient manner.
Innovation Solution
The method involves receiving data indicative of a path along a 3D point cloud, generating lists of 3D data points that intersect this path, identifying characteristics associated with surface features, grouping these characteristics, and ultimately identifying the surface feature based on the grouped properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If 3D point cloud data is used to capture comprehensive spatial information, then the amount of 3D data increases, but processing complexity and time consumption increase significantly
Solution Approach 1:
The patent segments the 3D point cloud data by generating multiple lists of 3D data points, where each list corresponds to a specific path through the point cloud. This segmentation divides the massive 3D dataset into manageable subsets that can be processed independently, reducing overall processing complexity while preserving spatial relationships within each segment.
Solution Approach 2:
The patent extracts relevant spatial relationship information from the 3D point cloud by identifying characteristics associated with surface features in each data list. This extraction process isolates the essential spatial information needed for feature identification while discarding redundant data, thereby reducing processing complexity without losing critical spatial relationships.
2Measurement precision
If traditional methods process 3D point cloud data directly, then feature identification is attempted, but processing time and resource requirements become excessive
Solution Approach 1:
The patent performs preliminary actions by generating organized lists of 3D data points along specified paths before conducting feature identification. This preprocessing step structures the raw 3D data into a more manageable format with inherent spatial organization, enabling faster subsequent processing while maintaining feature identification accuracy.
Solution Approach 2:
The patent introduces an intermediary structure in the form of organized data lists that serve as a bridge between raw 3D point cloud data and feature identification algorithms. These intermediary lists preserve spatial relationships while presenting data in a format optimized for efficient processing, thereby reducing processing time without sacrificing accuracy.
3Loss of information
If 3D point cloud data is processed without structured organization, then data completeness is maintained, but processing efficiency decreases
Solution Approach 1:
The patent segments the 3D point cloud into multiple organized lists based on spatial paths, maintaining data completeness within each segment while enabling efficient parallel or sequential processing. This segmentation preserves all original 3D data points and their relationships while structuring them for improved processing efficiency.
Solution Approach 2:
The patent organizes 3D point cloud data by introducing a path-based dimensional structure, where data points are grouped into lists according to their spatial trajectories. This dimensional reorganization maintains complete 3D information while adding a processing-friendly structure that significantly improves processing efficiency.
Data Source
AI summary
The techniques described herein relate to methods, apparatus, and computer readable media configured to identify a surface feature of a portion of a three-dimensional (3D) point cloud. Data indicative of a path along a 3D point cloud is received, wherein the 3D point cloud comprises a plurality of 3D data points. A plurality of lists of 3D data points are generated, wherein: each list of 3D data points extends across the 3D point cloud at a location that intersects the received path; and each list of 3D data points intersects the received path at different locations. A characteristic associated with a surface feature is identified in at least some of the plurality of lists of 3D data points. The identified characteristics are grouped based on one or more properties of the identified characteristics. The surface feature is identified based on the grouped characteristics.