Line-structured light scanning offers significant advantages in speed, precision, interference resistance, and applicability to medium- and large-scale industrial object scanning. Compared to other laser scanning systems, the laser galvanometer system features a simple structure, lower cost, higher single-reconstruction efficiency, and notable benefits in both reconstruction accuracy and real-time performance, which makes it an effective method for the rapid acquisition of surface features. We aim to address critical issues in traditional laser galvanometer-based three-dimensional (3D) reconstruction systems, such as low reconstruction efficiency, challenges in calibrating the light plane in multi-line systems, and significant errors in laser center-point matching. Unlike single-line scanning, multi-line laser scanning can acquire multiple laser stripes simultaneously, thereby significantly enhancing point cloud density and coverage. Moreover, compared to traditional monocular and binocular reconstruction methods, the trinocular reconstruction approach offers greater stability and robustness in terms of matching, error control, and precision, which effectively resolves the matching errors and occlusion problems commonly encountered in binocular systems under complex conditions. Therefore, research on the trinocular multi-line laser galvanometer scanning method is crucial for improving industrial measurement accuracy and achieving precise reconstruction of complex object surfaces.

We present a trinocular multi-line laser galvanometer system and propose a spatial-geometry-constrained laser center-point matching method. First, the three-view geometric projection matrix (TGPM) is estimated using matching points from the three views on a standard target surface, then optimized via the Levenberg?Marquardt (LM) algorithm to establish an accurate mapping between 3D space and two-dimensional (2D) image projections. Next, refined laser points from the three views are obtained using a sub-pixel-level laser stripe centerline extraction algorithm. A hybrid 2D?3D constraint method—integrating epipolar geometric slope constraints with 3D Euclidean distance consistency constraints—is then employed to achieve coarse matching of laser center points across the three views. Specifically, candidate matches for the left-view laser points in the middle and right views are first generated using epipolar geometric slope constraints; these initial matches are then reconstructed in 3D space, and coarse matching triplets are determined by enforcing 3D Euclidean distance consistency constraints. Finally, a fine matching strategy is applied to eliminate mismatched points by combining TGPM projection error constraints with laser stripe positional index consistency constraints. The coarse matching triplets are further refined through TGPM projection error enforcement, and the positional indices of multi-line laser stripes in the images are used to verify the consistency of laser stripe indices across the three views. Only matches with consistent stripe indices are retained for 3D point cloud reconstruction, which ensures that the matched points correspond to the same laser stripe in all views. This hierarchical coarse-to-fine framework significantly enhances the accuracy and robustness of 3D reconstruction.

Experimental validation of the proposed system demonstrates a 91.71% matching accuracy under 7-line laser conditions (Table 2), with strong adaptability and stability observed in center-point matching tasks for both 11-line and 15-line configurations (Fig. 9, Table 3). Repeated measurements on a step-standard component reveal height errors below 0.061457 mm (Table 5). The system achieves high-quality reconstructions of plaster statues, highly reflective cups, and culturally significant artifacts with complex textures (Figs. 12?14). Moreover, by leveraging compute unified device architecture (CUDA)-based graphics processing unit (GPU) acceleration, the system achieves single-frame reconstruction in 15.080 ms during 7-line scanning, which demonstrates superior efficiency (Table 6).

The trinocular multi-line laser galvanometer 3D reconstruction method, based on spatial geometric constraints proposed in this study, significantly enhances matching accuracy and reduces mismatched points by integrating epipolar geometric slope constraints, Euclidean distance consistency constraints, TGPM projection error constraints, and laser stripe index consistency constraints. This approach effectively addresses the limitations of traditional systems in light-plane calibration and laser point matching, eliminating the need for complex multi-line plane calibration during 3D reconstruction. A hierarchical coarse-to-fine matching strategy is adopted to minimize the mismatching rate of multi-line laser center points, thereby achieving high-precision and robust reconstruction results that meet the stringent accuracy requirements of industrial measurements. Furthermore, CUDA-based GPU acceleration is employed to significantly optimize computational efficiency, which makes the method suitable for large-scale industrial measurement tasks by reducing reconstruction time and improving productivity. Despite the notable advancements in multi-line laser galvanometer 3D reconstruction achieved in this study, several limitations warrant further optimization and investigation: 1) The current method is primarily designed for static scenarios; when objects or the scanning system are in motion, laser stripes may deform or cause ambiguous matching, compromising matching accuracy and reconstruction quality. 2) Although the method performs well under standard experimental conditions, its robustness may be affected by complex illumination environments (e.g., intense ambient light interference or dynamic lighting variations), thus leading to reduced precision in laser stripe extraction. The proposed 3D reconstruction method holds broad application potential across diverse fields, including industrial metrology, cultural heritage preservation, medical imaging, robotic vision, and reverse engineering. With ongoing advancements in hardware computational capabilities and intelligent algorithms, this approach is poised to play a pivotal role in practical applications and further drive the development of high-precision 3D reconstruction technologies.