Point Cloud Compression Using Topological Features for Joint Sensing
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Solution Overview
Problem
In multi-device joint sensing scenarios, point cloud data exchanged between devices and central nodes occupies a large quantity of air interface communication resources, necessitating a more efficient compression and transmission method.
Innovation Solution
A method involving extraction of topological features from point cloud data, followed by compression based on persistent homology and geometric plane structures, utilizing clustering planes and coordinate system mappings to reduce data volume.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is transmitted directly without compression, then data accuracy is maintained, but communication resource consumption increases significantly
Solution Approach 1:
The patent extracts and transmits only the essential topological feature information from the complete point cloud data. By identifying and transmitting only the critical topological characteristics (such as persistent homology features) rather than all spatial data points, the system significantly reduces data volume while preserving the fundamental structural information needed for accurate environment reconstruction and target recognition.
Solution Approach 2:
The patent introduces topological feature extraction as an intermediary processing layer between the point cloud data generation and transmission stages. This intermediary transformation converts complex spatial point cloud data into simplified topological feature representations, which serve as the actual transmitted data. This intermediary step enables substantial data reduction while maintaining the semantic meaning and accuracy required for downstream tasks.
2Loss of energy
If topological feature extraction and compression are applied to point cloud data, then communication resource usage is optimized, but processing complexity increases
Solution Approach 1:
The patent replaces traditional mechanical/geometric processing methods with topological data analysis techniques. Instead of processing individual spatial points and their geometric relationships, the system uses topological methods (such as persistent homology) to directly analyze and extract invariant topological features. This substitution simplifies the processing by working with higher-level abstract representations that are more resilient to noise and transformations, thereby reducing overall processing complexity despite the advanced mathematical tools involved.
3Measurement precision
If multi-device joint sensing is implemented, then sensing precision is improved, but data transmission burden increases
Solution Approach 1:
The patent merges the topological feature extraction and compression operations across multiple sensing devices before data transmission. Each device independently extracts topological features from its local point cloud data, and these compressed feature representations are then transmitted to the central node. This merging approach allows multiple devices to contribute their sensing information in a compact form, maintaining the precision benefits of multi-device sensing while dramatically reducing the total data transmission burden compared to transmitting complete point cloud datasets from all devices.
Data Source
AI summary
Embodiments of this disclosure provide a communication method and apparatus, a computer-readable storage medium, and a computer program product. In the method, a topological feature is extracted from point cloud data; the point cloud data is compressed based on the topological feature, to obtain compressed data of the point cloud data; and the compressed data is output. In this way, embodiments of this disclosure can reduce a data amount of reporting the point cloud data in a sensing scenario.