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Large-scale three-dimensional object measurement: a practical coordinate mapping and image data-patching method.

W Li, X Su, Z Liu

|March 25, 2008

Summary

This summary is machine-generated.

This study introduces new algorithms for accurate 3-D coordinate mapping and image patching in large-scale object measurement. The methods enable precise reconstruction of complex shapes by combining data from multiple sensor positions.

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Area of Science:

3-D Sensing and Reconstruction
Computer Vision
Metrology

Background:

Large-scale object measurement requires combining data from multiple subregions.
Accurate 3-D coordinates for each subregion are crucial for overall object reconstruction.
Existing methods for patching subregion data can be suboptimal.

Purpose of the Study:

To develop a novel phase-to-height mapping algorithm for precise 3-D coordinate acquisition.
To introduce an accurate lateral coordinate calibration method for subregion measurements.
To present an image data-patching method for seamless integration of local 3-D data into global coordinates.

Main Methods:

A new phase-to-height mapping algorithm was developed.
An accurate lateral coordinate calibration technique was implemented.
A flood algorithm-based image data-patching method was proposed for optimal subregion integration.

Main Results:

The proposed methods accurately obtain 3-D coordinates for subregions.
The flood algorithm provides an optimal patching path for integrating local data.
Successful measurement and patching of a large sandy pool (9m x 5m) demonstrated feasibility.

Conclusions:

The developed algorithms and patching method are reliable for large-scale 3-D sensing.
This approach enhances the accuracy and efficiency of reconstructing complex, large objects.
Experimental validation confirms the practical applicability of the proposed system.