Point Cloud Sampling and Format Conversion for Lower Data Volume
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Solution Overview
Problem
The large data amount of point clouds, particularly 3D point clouds with floating-point operation formats, necessitates increased storage capacity and bandwidth requirements, leading to higher costs for electronic devices.
Innovation Solution
A processing system that includes a sampling rate controller to adjust sampling rates based on output bandwidth and buffer capacity, a spatial region of interest processor to discard coordinates outside a defined area, a fixed-point number processor to convert floating-point formats, and a dimension reduction processor to reduce dimensions, thereby reducing data volume.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If floating-point operation format is used for 3D coordinates, then measurement precision is improved, but data amount increases
Solution Approach 1:
The patent changes the numerical representation parameter from floating-point format to fixed-point format for coordinate data. This parameter change reduces the bit width required to represent each coordinate while maintaining sufficient precision for the application, thereby reducing the overall data amount without completely sacrificing measurement precision.
Solution Approach 2:
The patent applies different data representation strategies to different parts of the coordinate system. By using fixed-point representation specifically for the coordinate values while potentially maintaining floating-point for other calculations, the system optimizes the local quality of data storage to reduce overall data amount while preserving necessary precision where required.
2Loss of information
If all coordinates are transmitted, then information completeness is improved, but bandwidth requirement increases
Solution Approach 1:
The patent extracts and transmits only the essential coordinate information that is necessary for the application. By identifying and removing redundant or less critical coordinate data, the system maintains the necessary information completeness while significantly reducing the bandwidth requirement for transmission.
Solution Approach 2:
The patent segments the coordinate data into different priority levels or categories, transmitting the most important segments first or with higher frequency. This segmentation allows the system to maintain information completeness for critical data while reducing overall bandwidth requirements by prioritizing essential information.
3Quantity of substance
If large buffer capacity is provided, then data storage capability is improved, but device cost increases
Solution Approach 1:
The patent changes the data representation parameters to reduce the storage capacity requirement. By using fixed-point format and optimizing the bit width for coordinate storage, the system achieves the necessary storage capability with a smaller buffer, thereby reducing device cost without sacrificing essential storage capacity.
Data Source
AI summary
A processing system for reducing data amount of a point cloud includes a sample rate controller and a transmitter. The sample rate controller is used for receiving a plurality of coordinates corresponding to the point cloud, and sampling the plurality of coordinates according to an adjustable sampling rate to generate a plurality of sampled coordinates, wherein data amount of the plurality of coordinates is not less than data amount of the plurality of sampled coordinates. The transmitter coupled to the sample rate controller is used for outputting the plurality of sampled coordinates.