Three-dimensional reconstruction technology has demonstrated broad application potential in the industrial, construction, medical, forestry, agricultural, and pastural sectors in the last few years. High-quality digital point cloud information exists to help researchers to understand objects and environments. However, current research mainly focuses on making adaptive adjustments to various scenarios and related issues in the application of this technology rather than looking for further improvements and enhancements based on technical principles. Meanwhile, a review of approaches, algorithms, and techniques for high-precision 3D reconstruction utilizing line-structured light scanning, which is analyzed from a deeper perspective of elementary details, is lacking. This paper takes the technological path as the logical sequence to provide a detailed summary of the latest development status of each key technology, which will serve potential users and new researchers in this field. The focus is placed on exploring studies reconstructing small-to-medium-sized objects, as opposed to performing large-scale reconstructions in the field.

The development of intelligent Vehicles (IVs) requires extensive standard datasets for training, benchmarking, and improvement. Autonomous Navigation and Mapping (ANM), as a critical technology for IVs, imposes exceptionally high demands on dataset construction. This is significant in its requirements for comprehensive sensor calibration, precise time synchronization, and accurate generation of ground truth. Besides, the whole construction workflow also demands intricate knowledge and sophisticated practices, necessitating lengthy learning curves for researchers to attain proficiency. The above challenges have led to a slow production of qualified datasets, directly constraining the advancement of ANM. However, so far, an investigation focused on a mature construction methodology of ANM dataset is still missing. This paper strives to fill the gap. Specifically, based on our systematic reviews and extensive practices, for the first time, a full-stack construction methodology of ANM dataset is proposed, including modules of platform construction, sensor calibration, time synchronization, ground truth generation, synthetic data production, and benchmark criteria, with detailed techniques and methodological routes provided in each step. Several long-standing issues are resolved within the methodology. Importantly, we introduce versatile calibration and synchronization frameworks that attain up to us-level and mm-level precision. Besides, we propose a full-scenario ground truth system that can generate scene-map and trajectory at cm-level accuracy. To verify the effectiveness of our methodology, we design a high-quality dataset and benchmark multiple state-of-the-art algorithms on it. The successful workflow demonstrates that our methodology can significantly reduce the research threshold and help individuals and institutions to construct datasets in a standardized way.

The Scheimpflug measurement system has broad application prospects in the fields of tomographic particle image velocimetry (PIV), ophthalmology, and three-dimensional reconstruction. Additionally, its special imaging structure makes the theoretical model of other traditional calibration methods no longer applicable. A cross-point calibration method in which the real-world coordinate system is transferred from an in-kind calibrator to displacement, achieving great improvements in the quantity and accuracy of the feature points, is proposed. This method, which makes up for the defects introduced by the distorted imaging plane, also in turn simplifies the calibration process. Finally, the proposed method is tested via both simulation and calibration experiments. The high calibration accuracy verifies that the method is feasible.

The advances in sensors and data processing technologies enrich the types of 3D point clouds acquirement, empowering numerous extensive and novel applications such as 3D reconstruction in various scenarios. However, the hole defects affect the accuracy and fidelity of the acquired point clouds, hindering further development and application of 3D point clouds. Aiming at the hole defects in point clouds, a Bayesian hole inpainting algorithm for the half-organized point cloud is proposed, where the point cloud is obtained by a structured-light section system. The algorithm establishes a Bayesian probability model in the hole region, which adopts specific distributions of the point cloud to estimate the maximum likelihood parameter. Simulation and experimental results show that the proposed approach outperforms other competing algorithms significantly in repairing various types of holes, both in objective and subjective qualities. In addition, the proposed algorithm has better scalabilities in the cases of wrong topology definition and self-intersection confusion. This is the first algorithm specially designed for hole inpainting in half-organized point clouds, which maximizes the comprehensive consideration of local features and global optimization, supplemented by targeted prior knowledge of density, Riemannian manifold, and discrete attributes.

Current monitoring of ubiquitous nodes in power grids is becoming increasingly important as the global trend accelerates toward digitization. Nonintrusive current monitoring using passive wireless systems offers a promising approach to avoid issues, such as the complexities of deployment considering insulation requirements in wired solutions and the battery maintenance inconveniences encountered in the existing wireless solutions. However, the limited energy in passive wireless sensing nodes shortens the length of the collected sensing data, leading to challenges in reconstructing the physical information, especially when the nonlinearity of the sensors is unignorable. Here, in this article, we present an end-to-end framework on top of the developed passive wireless sensing system, spanning from sensing data acquisition to current waveform reconstruction. By exploiting the nonquadratic features of the sensor, the nonlinearity-caused lost polarity of the bipolar current-induced magnetic field in the sensing data is recovered using a bidirectional long short-term memory (LSTM) model, which further achieves the current waveform reconstruction as a nonlinear regression. Moreover, the synchronization of the sensing data and its ground truth current waveform are performed by phase estimation using polynomial fitting on the sensing data, addressing the model training challenges caused by the asynchrony between the two because of the passive wireless sensing mechanism. Experimental results show that the quoted errors of the current waveform reconstruction are within 0.68%, and the normalized correlation coefficients are above 0.99991. Our work provides a promising comprehensive solution to enable the evolution of the power grid to complete digitalization. It can be extended further to other physical quantity monitoring applications, empowering global digital intelligence.