Qiwen Zhang

In the field of fringe projection profilometry, phase sensitivity is a critical factor influencing the precision of object measurements. Traditional techniques that employ basic horizontal or vertical fringe projection often do not achieve optimal levels of phase sensitivity. The identification of the fringe angle that exhibits optimal phase sensitivity has been a significant area of research. The present study introduces a novel method for determining the optimal fringe angle, facilitating 3D reconstruction without the need for equipment adjustments. Initially, the optimal fringe is derived through mathematical analysis, and the system’s position within each coordinate system is standardized, leading to the determination of the optimal fringe angle in the world coordinate system. Subsequently, an optimal fringe pattern, akin to that produced by a rotating projector, is generated based on the concept of rotation around a central point, with corresponding adjustments made to the calibration parameters. Finally, the optimal fringe is projected onto the target object for 3D reconstruction, thereby validating the proposed method. The experimental results demonstrate that this approach accurately identifies the optimal fringe angle, significantly enhancing both phase sensitivity and measurement accuracy. The accuracy of the measurement is significantly greater, by an order of magnitude, compared to the traditional method, with the error being approximately 50% of that associated with the currently established improved method.

A bullet point cloud registration algorithm with a low overlap rate based on line feature detection was proposed to solve the problem of the difficulty and low efficiency of point cloud registration due to the low overlap rate among point clouds sampled by the bullet model. In this paper, voxel downsampling is used to remove some noise points and outliers from the bullet point cloud and applied to the specified resolution to reduce the calculation cost. The bullet point cloud is transformed to a better initial position by fitting the central axis with the geometrical features of the bullet. Then, the direction vector of the bullet linear features is obtained by using an icosahedral fitting discrete Hough transform to simplify the parameter space of the search transformation. Finally, the optimal rotation angle is searched for in the parameter space by using the improved Cuckoo algorithm to realize the registration of the bullet point cloud with a low overlap rate. Simulation and experimental results show that the proposed registration method can accurately register bullet point clouds of different densities with a low overlap rate. Compared with the commonly used ICP, GICP, and TRICP algorithms, the registration error of the proposed algorithm is reduced by 92.68% on average when the overlap rate is 52.85%. The registration error is reduced by 98.87% in the case of a 41.36% overlap rate, by 99.52% in the case of a 33.02% overlap rate, and by 98.89% in the case of a 22.75% overlap rate.

Point cloud registration is widely used in autonomous driving, SLAM, and 3D reconstruction, and it aims to align point clouds from different viewpoints or poses under the same coordinate system. However, point cloud registration is challenging in complex situations, such as a large initial pose difference, high noise, or incomplete overlap, which will cause point cloud registration failure or mismatching. To address the shortcomings of the existing registration algorithms, this paper designed a new coarse-to-fine registration two-stage point cloud registration network, CCRNet, which utilizes an end-to-end form to perform the registration task for point clouds. The multi-scale feature extraction module, coarse registration prediction module, and fine registration prediction module designed in this paper can robustly and accurately register two point clouds without iterations. CCRNet can link the feature information between two point clouds and solve the problems of high noise and incomplete overlap by using a soft correspondence matrix. In the standard dataset ModelNet40, in cases of large initial pose difference, high noise, and incomplete overlap, the accuracy of our method, compared with the second-best popular registration algorithm, was improved by 7.0%, 7.8%, and 22.7% on the MAE, respectively. Experiments showed that our CCRNet method has advantages in registration results in a variety of complex conditions.