Point Cloud Data Management Layered Grid Structure
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Solution Overview
Problem
The management of large data sets from high-speed scanners, such as those used in generating point clouds for three-dimensional environments, faces challenges in storage, transfer, processing, visualization, and data integrity due to their massive size and sequential structure, which hinders efficient data manipulation and visualization.
Innovation Solution
The data is processed and stored using a layered grid structure where each point is represented by multiple components, segmented into patches, and compressed using codecs specific to each component, allowing for efficient storage, transfer, and visualization, and enabling recovery from interrupted scans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in sequential structure, then data integrity is maintained, but processing speed decreases
Solution Approach 1:
The patent divides the point cloud data into multiple layers, where each layer represents a different attribute (e.g., x-coordinate, y-coordinate, z-coordinate, intensity). This segmentation allows independent processing of each layer, improving processing speed while maintaining data integrity through structured organization.
Solution Approach 2:
The patent transforms the sequential one-dimensional data structure into a multi-dimensional layered structure. By organizing data across multiple layers corresponding to different spatial coordinates and attributes, the system enables parallel processing operations while preserving the complete point cloud information.
2Quantity of substance
If compression is applied to reduce data size, then storage efficiency improves, but data retrieval complexity increases
Solution Approach 1:
The patent segments point cloud data into multiple attribute layers (x, y, z coordinates, intensity, etc.), allowing selective compression and retrieval of specific layers. This segmentation reduces overall data size while simplifying retrieval operations, as users can access only the required layers without decompressing the entire dataset.
Solution Approach 2:
The patent enables extraction of specific attribute layers from the compressed multi-layer structure. Users can retrieve only the necessary components (e.g., only position data or only intensity data) without processing the complete dataset, reducing retrieval complexity and improving efficiency.
3Measurement precision
If high-density point measurements are collected, then measurement precision improves, but data processing time increases
Solution Approach 1:
The patent segments high-density point cloud data into multiple attribute layers, enabling parallel processing of each layer. This segmentation maintains the high measurement precision of the original data while reducing processing time through concurrent operations on separate layers.
Solution Approach 2:
The patent allows processing of only the necessary subset of layers based on the specific application requirements. Users can perform partial processing on relevant attributes without processing the entire high-density dataset, reducing processing time while maintaining precision for the required measurements.
Data Source
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AI summary
Methods and apparatus are provided for processing of data representing points in space wherein each is represented by components defining its position in a coordinate system and at least one parameter. For each point, the data are separated into a layer per component, and each component is assigned to a cell of a two-dimensional grid of cells such that corresponding cells of multiple layers contain the components of a point. A component of a point is retrieved by reference to a grid position corresponding to the point and to a layer corresponding to the component. Each layer is segmented into patches of cells such that a component of a point can be retrieved by reference to a grid position of a patch within a layer and to a grid position of a cell within a patch. A layer is compressed using an associated codec.