3D Display Calibration by Visual Pattern Analysis

TL;DR

This paper introduces a novel calibration method for 3D displays using pattern analysis in frequency domain, achieving high accuracy, efficiency, and robustness through dual-image capture and quantitative modeling.

Contribution

The proposed method accurately calibrates 3D display parameters by analyzing captured pattern images in frequency domain, outperforming prior techniques in speed and noise robustness.

Findings

Calibration accuracy improved by about half an order of magnitude.

Calibration process takes less than 2 seconds.

Method remains effective at low SNR levels as low as 6 dB.

Abstract

Nearly all 3D displays need calibration for correct rendering. More often than not, the optical elements in a 3D display are misaligned from the designed parameter setting. As a result, 3D magic does not perform well as intended. The observed images tend to get distorted. In this paper, we propose a novel display calibration method to fix the situation. In our method, a pattern image is displayed on the panel and a camera takes its pictures twice at different positions. Then, based on a quantitative model, we extract all display parameters (i.e., pitch, slanted angle, gap or thickness, offset) from the observed patterns in the captured images. For high accuracy and robustness, our method analyzes the patterns mostly in frequency domain. We conduct two types of experiments for validation; one with optical simulation for quantitative results and the other with real-life displays for qualitative assessment. Experimental results demonstrate that our method is quite accurate, about a half order of magnitude higher than prior work; is efficient, spending less than 2 s for computation; and is robust to noise, working well in the SNR regime as low as 6 dB.