Camera parameters can’t be estimated accurately using traditional calibration methods if the camera is substantially defocused. To tackle this problem, an improved approach based on three phase-shifting circular grating (PCG) arrays is proposed in this paper. Rather than encoding the feature points into the intensity, the proposed method encodes the feature points into the phase distribution, which can be recovered precisely using phase-shifting methods. The PCG centers are extracted as feature points, which can be located accurately even if the images are severely blurred. Unlike the previous method which just uses a single circle, the proposed method uses a concentric circle to estimate the PCG center, such that the center can be located precisely. This paper also presents a sorting algorithm for the detected feature points automatically. Experiments with both synthetic and real images were carried out to validate the performance of the method. And the results show that the superiority of PCG arrays compared with the concentric circle array even under severe defocus.

Binary coding methods have been widely used for three-dimensional (3D) shape measurement. However, traditional binary coding methods always require a sequence of binary patterns to encode the fringe order. This paper presents a spatial-temporal binary coding (STBC) method for 3D shape measurement with three binary patterns, including a spatial pattern and two temporal patterns. Specifically, the spatial pattern is used to encode a 4-bit spatial code, and the temporal patterns are used to encode the 2-bit temporal code. By combining the spatial code and the temporal code, 6-bit code words can be obtained that distinguish 2 6 = 64 fringe periods of sinusoidal phase-shifting patterns. A robust dual-region labeling decoding scheme is also developed to extract the 6-bit code word for fringe order determination, and then absolute-phase unwrapping can be carried out. Experiments have been conducted, and their results confirm that the STBC method can reliably reconstruct the 3D shapes of measured objects with only three binary patterns.

Event camera calibration plays an irreplaceable role in event camera-based visual applications. Event cameras report per-pixel brightness changes as a stream of asynchronous events, making them insensitive to the spatial distribution of feature points. Consequently, traditional methods such as checkerboard and circle calibration algorithms, which rely on spatial intensity distribution for feature point extraction, are ill-suited for event camera calibration. To address this issue, a flexible and accurate event camera calibration method is presented in this work. The method analyzes the frequency characteristics of event data streams, introducing the groundbreaking use of Fourier transform in event camera calibration. Instead of relying on intensity, we extract feature points from the robust phase domain, which mitigates the impact of event camera noise. Event frames are derived from the event data streams, and we recover two crossed phase maps using Fourier transform. Feature points are then detected using the law of phase distribution and refined with sub-pixel accuracy. Subsequently, camera parameters are estimated. The performance of our method was rigorously validated from different perspectives, and the results show that the accuracy of proposed method is substantially improved compared to the conventional methods. The root mean square errors (RMSE) of the proposed method are consistently below 0.06 pixels, with some cases achieving an impressive 0.03 pixels. The accuracy of binocular reconstruction reaches 0.394 mm.