In structured light systems, the accuracy of measurement notably diminishes when assessing complex texture objects, especially encountering boundaries between various colors. To address this challenge, this paper meticulously analyzes and establishes an error model, elaborating the correlation between phase errors and the gradients of phase and gray-scale. Based on this analysis, a novel high-precision method is proposed for measuring complex texture objects via bidirectional fringe projection. This approach firstly leverages horizontal and vertical fringe projections to derive bidirectional phase information and calculates the angles between the tangent of the texture edges and the phase gradient. Subsequently, a refined temporal phase correction algorithm is formulated based on the epipolar matching algorithm and the devised error model, effectively mitigating numerical instability issues within the algorithm and significantly reducing errors of bidirectional phases. Ultimately, corrected point clouds are calculated based on bidirectional phases, and the obtained point clouds are merged to further diminish phase errors. Comparison experiments indicate that this method can reduce Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 65.74% and 67.75%, respectively. Compared to existing methods, it improves performance by 27.29% and 33.74%, respectively, demonstrating superior performance.

3D reconstruction is a fundamental task in robotics and AI, providing a prerequisite for many related applications. Fringe projection profilometry is an efficient and non-contact method for generating 3D point clouds out of 2D images. However, during the actual measurement, it is inevitable to experiment with translucent objects, such as skin, marble, and fruit. Indirect illumination from these objects has substantially compromised the precision of 3D reconstruction via the contamination of 2D images. This paper presents a fast and accurate approach to correct for indirect illumination. The essential idea is to design a highly suitable network architecture founded on a precise error model that facilitates accurate error rectification. Initially, our method transforms the error generated by indirect illumination into a sine series. Based on this error model, the multilayer perceptron is more effective in error correction than traditional methods and convolutional neural networks. Our network was trained solely on simulated data but was tested on authentic images. Three sets of experiments, including two sets of comparison experiments, indicate that the designed network can efficiently rectify the error induced by indirect illumination.

A newly developed calibration algorithm for camera-projector system using spheres is presented in this paper. Previous studies have exploited image conics of sphere to calibrate the camera, whereas this approach can be strengthened to apply in the projector and ultimately achieve the overall calibration for single or multiple pairs of camera-projector. Following the concept of taking the projector as an inverse camera, we retrieve the image conic of the sphere on the projector plane based on a pole-polar relationship we found. At least 3 image conics on the image plane of each device are required to calculate the intrinsic parameters of the device. The extrinsic parameters for all devices in the system are determined by the position of sphere centers in each coordinates frame of the device. Based on the isotropy of the calibration object (sphere), this work is mainly interested in accomplishing the entire calibration for multiple camera-projector systems in which sensors surround a central observation volume. Experiments are conducted on both synthetic and real datasets to evaluate its performance.