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Efficient point cloud lossless data compression method based on an embedded Gray code structured light pattern

Hossein Rashidizad, MohmmadMorad Sheikhi, Gholamreza Akbarizadeh

|November 22, 2018

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This study introduces a novel lossless data compression method for 3D point clouds, crucial for instrumentation engineering. The technique achieves significant compression ratios, addressing data size limitations in 3D scanning operations.

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Area of Science:

Instrumentation Engineering
Computer Vision
Data Compression

Background:

3D scanning techniques have advanced significantly, improving speed and accuracy across industries like quality control and reverse engineering.
However, the large data size of 3D point clouds poses challenges for data transfer, storage, and real-time processing.
Efficient data compression is essential for leveraging the full potential of 3D scanning technologies.

Purpose of the Study:

To develop a novel, lossless data compression method for 3D point cloud data.
To address the limitations imposed by large point cloud file sizes in 3D scanning applications.
To improve the practicality and efficiency of 3D scanning operations in instrumentation engineering.

Main Methods:

A novel lossless data compression technique for point clouds was developed.
The method utilizes a Gray code structured light pattern sequence.
Image-based compression strategies were integrated into the point cloud compression pipeline.

Main Results:

The proposed method demonstrated a distinct compression ratio compared to existing lossless point cloud compression techniques.
Empirical evaluations confirmed the reliability and practicality of the developed compression approach.
The technique effectively reduces point cloud data size without information loss.

Conclusions:

The novel Gray code structured light pattern sequence and image-based compression method offers a practical solution for lossless point cloud compression.
This advancement is vital for overcoming data size restrictions in 3D scanning, enhancing applications in instrumentation engineering.
The method achieves a competitive compression ratio, making 3D scanning data more manageable.