3D Point Cloud Encoding Using Angle-Based Arithmetic Models
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Solution Overview
Problem
Current methods for encoding and decoding three-dimensional data lack efficiency, particularly in representing and transmitting large amounts of point cloud data, which is essential for applications like autonomous vehicles and infrastructure inspection.
Innovation Solution
A method that determines a reference three-dimensional point based on angles and distance to perform arithmetic encoding or binarizing, adapting the encoding scheme according to the angle to improve encoding efficiency by reducing data amount and processing load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is stored and transmitted as-is, then data completeness is maintained, but data volume becomes excessively large
Solution Approach 1:
The patent applies local quality by differentiating the encoding treatment based on the first angle of each three-dimensional point. Points are encoded using different arithmetic models depending on their angular characteristics, allowing efficient compression while preserving essential spatial information. This selective encoding approach reduces overall data volume without compromising critical geometric data.
Solution Approach 2:
The patent changes the encoding parameters dynamically based on the first angle of each point. By selecting different arithmetic models (e.g., exponential Golomb code, truncated unary code) according to angular parameters, the system optimizes compression efficiency for each point while maintaining data integrity. This parameter adaptation enables effective data reduction without information loss.
2Productivity
If uniform encoding is applied to all three-dimensional points, then processing simplicity is maintained, but encoding efficiency decreases
Solution Approach 1:
Instead of applying uniform encoding, the patent implements local quality by tailoring the arithmetic model to the specific characteristics of each point based on its first angle. This allows high encoding efficiency for points with similar angular properties while maintaining manageable complexity through systematic classification of encoding models.
Solution Approach 2:
The patent segments the encoding process into distinct arithmetic models based on angular ranges. By dividing the angular space into segments and assigning appropriate encoding models to each segment, the system achieves high efficiency without excessive complexity. The segmentation allows parallel processing and simplifies the overall encoding architecture.
3Measurement precision
If more detailed encoding is applied to capture all point characteristics, then data precision is improved, but processing time increases
Solution Approach 1:
The patent changes encoding parameters based on the first angle to achieve an optimal balance between precision and processing time. By selecting arithmetic models that provide sufficient precision for each angular region, the system avoids unnecessary computational complexity while maintaining accurate point cloud representation. This adaptive approach ensures fast processing without sacrificing critical precision.
Solution Approach 2:
The patent applies partial action by encoding only the necessary angular information (first angle) in detail while using simpler models for other attributes. This selective detailed encoding captures essential spatial relationships without the processing overhead of fully detailed encoding for all parameters, thus reducing processing time while maintaining adequate precision.
Data Source
AI summary
A three-dimensional data decoding method includes: obtaining a bitstream including encoded three-dimensional points generated by encoding three-dimensional points each of which is represented by a first angle, a second angle, and a distance; determining a reference three-dimensional point among the three-dimensional points; and performing, according to the first angle of the reference three-dimensional point, at least one of arithmetic decoding or debinarizing on encoded information of a current encoded three-dimensional point included in the encoded three-dimensional points to generate information of a current three-dimensional point included in the three-dimensional points, the information excluding the first angle.