Self-adaptive Point Cloud Stripe Division for Parallel Processing

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Solution Overview

Problem

Current point cloud data processing faces challenges in efficient storage and transmission due to large data volumes, and existing strip division technologies are not well-developed, limiting parallel processing and system fault tolerance.

Innovation Solution

A self-adaptive point cloud stripe division method that uses octree division and Morton code sorting to create local point clouds, adjusts their size through re-segmentation and fusion to meet specific point number ranges, supporting parallel processing and efficient coding.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If large-scale point cloud data is processed as a whole, then processing completeness is maintained, but storage and transmission pressure increases significantly

Engineering Contradiction:

Improveprocessing completenessVSAvoiddata volume

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The patent divides large-scale point cloud data into multiple independent point cloud strips based on spatial coordinates and Morton code ordering. This segmentation allows each strip to be processed, stored, and transmitted independently, reducing the storage and transmission pressure while maintaining the ability to reconstruct the complete point cloud data through concatenation of all strips.

Inventive Principle:

Principle #1Segmentation

2Productivity

If point cloud data is divided into strips for parallel processing, then processing efficiency improves, but system complexity increases

Engineering Contradiction:

Improveparallel processing efficiencyVSAvoiddivision system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent performs preliminary sorting of point cloud data using Morton code before division. This preliminary action organizes the data in a specific spatial order, which simplifies the subsequent strip division process and makes the division rules more straightforward, thereby reducing the overall system complexity while enabling efficient parallel processing.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements dynamic adjustment of strip division parameters based on data distribution characteristics. The division strategy can adapt to different point cloud densities and spatial distributions, optimizing the balance between parallel processing efficiency and system complexity for different scenarios.

Inventive Principle:

Principle #15Dynamics

3Ease of manufacture

If uniform strip division is applied, then division simplicity is maintained, but adaptability to different data distributions deteriorates

Engineering Contradiction:

Improvedivision simplicityVSAvoiddata distribution adaptability

Core Design Contradiction:

Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The patent employs different division strategies for different regions of the point cloud data based on local characteristics. By combining Morton code sorting with adaptive division rules, the system applies uniform division where data is evenly distributed while using adaptive division where data density varies significantly, thus maintaining simplicity where possible while improving adaptability where needed.

Inventive Principle:

Principle #3Local quality

4Adaptability or versatility

If non-uniform strip division is applied, then adaptability to data distribution improves, but division complexity increases

Engineering Contradiction:

Improvedata distribution adaptabilityVSAvoiddivision system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11875513B2Self-adaptive point cloud stripe division method

Publication Date: 2024.01.16 PEKING UNIV SHENZHEN GRADUATE SCHOOL

AI summary

A self-adaptive point cloud stripe division method. The method comprises: firstly, carrying out space division with a certain depth on a point cloud to obtain a plurality of local point clouds; then, counting the number of points in each of the local point clouds, comparing same with an upper and lower limit for the number of stripe points, and determining whether the number of points satisfies a requirement; and after a series of re-segmentation or re-fusion operations on the local point clouds, adjusting the number of points in each of the local point clouds until the number of points satisfies a range, thereby obtaining a final point cloud stripe. A plurality of local structures capable of being independently coded and decoded are obtained by means of division of a point cloud stripe, and this supports parallel processing, enhances system fault tolerance, and improves coding efficiency. Moreover, in consideration of the fact that the size of a point cloud stripe often has a limited range due to realistic factors of a large-scale point cloud, such as a limited transmission bandwidth, the method can satisfy such a requirement and make it possible to generate a plurality of point cloud stripes with a rational number of points while supporting parallel processing.