Point Cloud Histogram Generation for Real-Time Object Detection
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Solution Overview
Problem
Conventional machine vision techniques face inefficiencies when processing 3D point cloud data due to the massive number of 3D points and lack of spatial relationships, making it challenging for real-time object detection and comparison.
Innovation Solution
Generating 1D and 2D histograms from 3D point cloud data based on distances to reference planes or orientations, allowing for data reduction and efficient comparison of objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If 3D point cloud data is processed directly, then comprehensive 3D information is available, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the 3D point cloud data into multiple 2D projection views (front, back, left, right, top, bottom). This segmentation transforms the complex 3D processing task into multiple simpler 2D processing tasks, reducing computational complexity while preserving essential spatial information through multi-angle representations.
Solution Approach 2:
The patent introduces 2D projection histograms as an intermediary representation between the original 3D point cloud and the final object recognition result. These histograms serve as compressed feature descriptors that capture spatial distribution characteristics without requiring processing of all individual 3D points, thus reducing computational burden while maintaining discriminative power.
2Loss of information
If all 3D points are processed, then complete spatial data is obtained, but processing complexity increases
Solution Approach 1:
The patent extracts essential spatial relationship information by projecting 3D points onto 2D planes and computing histogram distributions. This extraction process isolates the most relevant spatial characteristics (density, distribution patterns, geometric features) while discarding redundant information, thereby simplifying processing complexity without significant loss of discriminative spatial relationships.
Solution Approach 2:
The patent transforms the high-dimensional 3D point coordinates into lower-dimensional histogram parameters that describe spatial distributions. By changing the representation parameters from individual point coordinates (x, y, z) to histogram bin counts representing spatial densities, the system reduces processing complexity while preserving essential spatial relationship patterns.
3Measurement precision
If 3D point cloud data is used for object comparison, then accurate 3D matching is possible, but computational cost increases
Solution Approach 1:
The patent creates 2D projection histogram copies of the 3D point cloud from multiple viewing angles. These histogram copies serve as efficient surrogate representations for object comparison, allowing accurate shape and structure analysis through 2D distributions rather than direct 3D point-to-point matching, thus significantly reducing computational cost while maintaining comparison accuracy.
Data Source
AI summary
The techniques described herein relate to methods, apparatus, and computer readable media configured to generate point cloud histograms. A one-dimensional histogram can be generated by determining a distance to a reference for each 3D point of a 3D point cloud. A one-dimensional histogram is generated by adding, for each histogram entry, distances that are within the entry's range of distances. A two-dimensional histogram can be determined by generating a set of orientations by determining, for each 3D point, an orientation with at least a first value for a first component and a second value for a second component. A two-dimensional histogram can be generated based on the set of orientations. Each bin can be associated with ranges of values for the first and second components. Orientations can be added for each bin that have first and second values within the first and second ranges of values, respectively, of the bin.