3D Point Cloud Data Partitioning and Real-Time R-Tree Indexing
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Solution Overview
Problem
Current laser distance-measuring systems generate high-density point clouds that are time-consuming to process due to the rapid data collection exceeding real-time processing capabilities, leading to significant delays in converting and visualizing 2D scan data into 3D formats.
Innovation Solution
A method involving a computer that determines a partition structure based on the size of the point cloud and a predetermined leaf-block size, allowing for real-time indexing and storage of data during collection, using a flexible index like an R-tree to efficiently manage and query 3D data blocks, enabling immediate access and manipulation after collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If laser scanners collect data at high speed (500,000 points/sec), then data collection rate is improved, but processing time increases significantly
Solution Approach 1:
The patent applies preliminary action by establishing the partition structure and indexing scheme before data collection begins. The 3D space is pre-divided into canonical blocks with predetermined boundaries, allowing data to be sorted and indexed during collection rather than after, thus eliminating the post-processing bottleneck
Solution Approach 2:
The patent segments the 3D data space into canonical blocks using a partition structure that divides space along x, y, and z axes. This segmentation allows data to be organized into manageable chunks during collection, enabling efficient storage and retrieval without requiring complete data sets before processing
2Ease of manufacture
If data is stored in 2D grid order as collected, then storage simplicity is improved, but 3D visualization capability deteriorates
Solution Approach 1:
The patent transitions from 2D grid storage to 3D canonical block organization by introducing a third spatial dimension for data arrangement. Data points are sorted into blocks based on their x, y, and z coordinates, creating a three-dimensional data structure that naturally supports 3D visualization while maintaining efficient storage through regular patterns
3Measurement precision
If complete data sets are required before indexing, then indexing accuracy is improved, but processing time increases
Solution Approach 1:
The partition structure and block boundaries are predetermined before data collection, allowing the system to prepare the indexing framework in advance. This preliminary setup enables continuous indexing during data collection without waiting for complete data sets
Solution Approach 2:
The data sorting and indexing process serves itself during collection by automatically placing points into appropriate canonical blocks as they arrive. The system maintains its own data organization structure without requiring external post-processing intervention
Data Source
AI summary
A high-density, distance-measuring laser system and an associated computer that processes the data collected by the laser system. The computer determines a data partition structure and stores that structure as a header file for the scan before data is collected. As the scan progresses, the computer collects data points until a predetermined threshold is met, at which point a block of data consisting of the data points up to the threshold is written to disk. The computer indexes each data block using all three coordinates of its constituent data points using, preferably, a flexible index, such as an R-tree. When a data block is completely filled, it is written to disk preferably with its index and, as a result, each data block is ready for access and manipulation virtually immediately after having been collected. Also, each data block can be independently manipulated and read from disk.