Accurate Camera Calibration Robust to Defocus Using a Smartphone

Abstract

We propose a novel camera calibration method for defocused images using a smartphone under the assumption that the defocus blur is modeled as a convolution of a sharp image with a Gaussian point spread function (PSF). In contrast to existing calibration approaches which require wellfocused images, the proposed method achieves accurate camera calibration with severely defocused images. This robustness to defocus is due to the proposed set of unidirectional binary patterns, which simplifies 2D Gaussian deconvolution to a 1D Gaussian deconvolution problem with multiple observations. By capturing the set of patterns consecutively displayed on a smartphone, we formulate the feature extraction as a deconvolution problem to estimate feature point locations in sub-pixel accuracy and the blur kernel in each location. We also compensate the error in camera parameters due to refraction of the glass panel of the display device. We evaluate the performance of the proposed method on synthetic and real data. Even under severe defocus, our method shows accurate camera calibration result.

A Robust and Accurate Calibration Method for Out-of-focus Camera

“…For binary-shift patterns, period is fixed to 36 and single step is fixed to 1. The frequency and step of phase-shift patterns are calculated based on the Eq (10) and Eq (11). For the sake of fairness, we calibrate the camera with this paper proposed method firstly, then we display and capture the chessboard pattern on the monitor with the same pose and position.…”

“…Finally, deblurred patterns are decoded to generate dense phase map to extract accurate feature points coordinates. Above steps make camera calibration robust to noises, glass refraction compared with [10] [11]. Experimental results demonstrate that our method improves calibration robustness greatly while keeps accuracy the same as the well-focused calibration.…”

“…Ha et al [11] estimated the blur kernel in each location by unidirectional complementary binary patterns and detected feature points' coordinates from 1D deconvolution. Due to the limitation of feature points' detection, it was sensitive to noises and refraction of the glass panel.…”

Camera Calibration with Phase-Shifting Wedge Grating Array

“…Among various existing patterns, squares [5][6][7][8], and circles [9][10][11][12] have become popular due to their ease of use. In order to avoid the influence of the fabrication to the calibration results and further simplify the calibration procedures, the digital displays have been applied to the camera calibration, which provide an alternative way for planar targets [13][14][15][16][17][18][19].…”

Augmented Reality in Medicine

“…Pinhole model is a simple camera model that maps the 3D real world onto twodimensional (2D) coordinates called the image plane. 3D points are mapped onto the 2D image plane by translating the point on a straight line towards the camera center until it intersects the image plane [8,9]. This mapping is called perspective projection, and the transformation between the image and real world coordinates can be represented as a projection matrix.…”