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Scene Depth Perception Based on Omnidirectional Structured Light.

Tong Jia, BingNan Wang, ZhongXuan Zhou

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 15, 2016

Summary

This study introduces a novel depth perception method using structured light and omnidirectional imaging. The technique achieves accurate, large-scale depth mapping with minimal error, improving 3D scene reconstruction.

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Area of Science:

Computer Vision
3D Imaging
Metrology

Background:

Accurate depth perception is crucial for 3D reconstruction and scene understanding.
Existing methods struggle with large-scale scenes and occlusions.
Omnidirectional imaging offers a wider field of view but requires specialized depth sensing.

Purpose of the Study:

To develop a robust depth perception method for large-scale omnidirectional imaging.
To improve the accuracy and reliability of structured light-based depth sensing.
To address challenges posed by occlusions and simplify system calibration.

Main Methods:

A novel structured light pattern, the four-direction sand clock-like (FDSC) image, was designed for enhanced positional accuracy.
A projector calibration method using multiple reference planes was developed for simplified calibration in omnidirectional systems.
An occlusion-avoiding depth point cloud matching algorithm based on prior constraint iterative closest point (PC-ICP) was implemented.

Main Results:

The proposed method successfully acquired omnidirectional depth information for large-scale scenes.
Experimental analysis showed a maximum measuring error of 0.53 mm and an average error of 0.25 mm across 16 depth data sets.
The FDSC pattern demonstrated superior robustness and accuracy compared to conventional patterns.

Conclusions:

The integrated approach of structured light and omnidirectional imaging provides effective large-scale depth perception.
The novel FDSC pattern and PC-ICP algorithm enhance accuracy and handle occlusions.
The simplified calibration method makes the system more practical for real-world applications.