Six-degree-of-freedom (6-DOF) pose estimation from feature correspondences remains a popular and robust approach for 3-D registration. However, heavy outliers that existed in the initial correspondence set pose a great challenge to this problem. This article presents a simple yet effective estimator called SAmple Consensus by sampling COmpatibility Triangles in graphs (SAC-COT) for robust 6-DOF pose estimation and 3-D registration. The key novelty is a guided three-point sampling approach. It is based on a novel correspondence sample representation, i.e., COmpatibility Triangle (COT). We first model the correspondence set as a graph with nodes connecting compatible correspondences. Then, by ranking and sampling COTs formed by ternary loops, we show that correct hypotheses can be generated in early iteration stage. Finally, the hypothesis generated by the COT yielding to the maximum consensus is the output of SAC-COT. Extensive experiments on six data sets and extensive comparisons with the state-of-the-art estimators confirm that: 1) SAC-COT can achieve accurate registrations with a few iterations and 2) SAC-COT outperforms all competitors and is ultrarobust when confronted with Gaussian noise, data decimation, holes, clutter, partial overlap, varying scales of input correspondences, and data modality variation.

Measuring objects with high dynamic range (HDR) reflectivity by coded structured-light, captured stripes are usually seriously distorted by reflectivity, causing inaccurate measurement results. A stripe enhancement method is proposed to deal with the problem. The method is based on the correspondence between phase and intensity of the stripe. First, the phase map of the captured stripe pattern is retrieved by phase-shift algorithm and multiexposure method, where saturation and low contrast of the stripe are eliminated; then, the modulation of stripes is normalized to eliminate the influence of reflectivity; finally, the enhanced stripe is obtained by assembling the modulation and the phase map. Experimental results demonstrate that the method is efficient for objects with HDR reflectivity and achieves high accuracy.

In the phase-measuring structured-light method, image saturation will induce large phase errors. Usually, by selecting proper system parameters (such as the phase-shift number, exposure time, projection intensity, etc.), the phase error can be reduced. However, due to lack of a complete theory of phase error, there is no rational principle or basis for the selection of the optimal system parameters. For this reason, the phase error due to image saturation is analyzed completely, and the effects of the two main factors, including the phase-shift number and saturation degree, on the phase error are studied in depth. In addition, the selection of optimal system parameters is discussed, including the proper range and the selection principle of the system parameters. The error analysis and the conclusion are verified by simulation and experiment results, and the conclusion can be used for optimal parameter selection in practice.

In fringe projection profilometry, highlight usually causes the saturation and blooming in captured fringes and reduces the measurement accuracy. To solve the problem, a regional-projection fringe projection (RP-FP) method is proposed. Regional projection patterns (RP patterns) are projected onto the tested object surface to avoid the saturation and blooming. Then, an image inpainting technique is employed to reconstruct the missing phases in the captured RP patterns and a complete surface of the tested object is obtained. Experiments verified the effectiveness of the proposed method. The method can be widely used in industrial inspections and quality controlling in mechanical and manufacturing industries.

In contrast to traditional phase-shifting (PS) algorithms, which rely on capturing multiple fringe patterns with different phase shifts, digital PS algorithms provide a competitive alternative to relative phase retrieval, which achieves improved efficiency since only one pattern is required for multiple PS pattern generation. Recent deep learning-based algorithms further enhance the retrieved phase quality of complex surfaces with discontinuity, achieving state-of-the-art performance. However, since much attention has been paid to understanding image intensity mapping, such as supervision via fringe intensity loss, global temporal dependency between patterns is often ignored, which leaves room for further improvement. In this paper, we propose a deep learning model-based digital PS algorithm, termed PSNet. A loss combining both local and global temporal information among the generated fringe patterns has been constructed, which forces the model to learn inter-frame dependency between adjacent patterns, and hence leads to the improved accuracy of PS pattern generation and the associated phase retrieval. Both simulation and real-world experimental results have demonstrated the efficacy and improvement of the proposed algorithm against the state of the art.