This paper proposes a point cloud post-processing method based on the minimum spanning tree (MST) and depth-first search (DFS) to extract laser stripe centerlines from the complex inner surfaces of deep holes. Addressing the limitations of traditional image processing methods, which are affected by burrs and low-frequency random noise, this method utilizes 360° structured light to illuminate the inner wall of the deep hole. A sensor captures laser stripe images, and the Steger algorithm is employed to extract sub-pixel point clouds. Subsequently, an MST is used to construct the point cloud connectivity structure, while DFS is applied for path search and noise removal to enhance extraction accuracy. Experimental results demonstrate that this method significantly improves extraction accuracy, with a dice similarity coefficient (DSC) approaching 1 and a maximum Hausdorff distance (HD) of 3.3821 pixels, outperforming previous methods. This study provides an efficient and reliable solution for the precise extraction of complex laser stripes and lays a solid data foundation for subsequent feature parameter calculations and 3D reconstruction.

Deep hole measurement is a crucial step in both deep hole machining and deep hole maintenance. Single-camera vision presents promising prospects in deep hole measurement due to its simple structure and low-cost advantages. However, the measurement error caused by the heating of the imaging sensor makes it difficult to achieve the ideal measurement accuracy. To compensate for measurement errors induced by imaging sensor heating, this study proposes an error compensation method for laser and vision-based deep hole measurement instruments. This method predicts the pixel displacement of the entire field of view using the pixel displacement of fixed targets within the camera’s field of view and compensates for measurement errors through a perspective transformation. Theoretical analysis indicates that the perspective projection matrix changes due to the heating of the imaging sensor, which causes the thermally induced measurement error of the camera. By analyzing the displacement of the fixed target point, it is possible to monitor changes in the perspective projection matrix and thus compensate for camera measurement errors. In compensation experiments, using target displacement effectively predicts pixel drift in the pixel coordinate system. After compensation, the pixel error was suppressed from 1.99 pixels to 0.393 pixels. Repetitive measurement tests of the deep hole measurement instrument validate the practicality and reliability of compensating for thermal-induced errors using perspective transformation.

In this study, a ring light point cloud calibration technique based on collimated laser beams is developed, aiming to reduce errors caused by the position and attitude changes of traditional ring light measurement devices. This article details the generation mechanism of the ring beam and the principle of deep hole measurement. It introduces the collimated beam as a reference, building on traditional ring light measurement devices, to achieve the synchronous acquisition of the ring beam and collimated spot images by an industrial camera. The Steger algorithm is employed to accurately extract the coordinates of the point cloud contours of both the ring beam and the collimated spot. By analyzing the shape and position changes of the collimated spot contour, the spatial position and attitude of the measuring device are precisely determined. This technique is applied to the 3D reconstruction of the inner surface of deep holes, ensuring the accurate restoration of the spatial positional attitude of the ring beam by incorporating the spatial positional attitude parameters of the measuring device to precisely calibrate the cross-sectional point cloud coordinates. Experimental results with ring gauges and deep hole workpieces demonstrate that this technique effectively reduces the percentage of point cloud data outside the tolerance range, and improves the accuracy of the 3D reconstruction model by 6.287%, thereby verifying the accuracy and practicality of this technique.

Extracting the laser stripe center is crucial for 3D reconstruction using laser triangulation. However, automatically extracting laser stripes from complex surfaces is a challenging task. In this work, we have improved upon the Steger method and introduced a new laser stripe identification technique called the Breakdown Discharging Method. This method transforms the laser stripe extraction process into an electrical breakdown problem, where the path of discharge across an insulating medium’s surface is tracked to extract the laser stripe centerline. The primary advantage of the Breakdown Discharging Method lies in its ability to optimize the extraction of laser stripe centerlines, even on complex surfaces where the stripe may be unevenly illuminated or discontinuous—tasks that existing methods struggle to accomplish. Results from various complex surfaces demonstrate that the Breakdown Discharging Method is highly robust, effectively adapting to laser stripes on intricate shapes. This novel extraction method will advance the application and development of 3D reconstruction for objects with complex shapes.

In the process of extracting the centerline of laser streaks in complex deep-hole cavities, burrs as well as cluttered low-frequency random noise often appear in the point cloud data acquired by traditional image processing methods. To address this issue, this study proposes a point cloud post-processing method based on the Minimum Spanning Tree (MST) and Depth-First Search (DFS) algorithms. First, the Steger algorithm is used to generate a subpixel point cloud model containing noise. Then, an effective connectivity structure of the point cloud is constructed using MST, and the DFS algorithm is applied for path searching of the main contour and noise elimination. The proposed method can accurately extract laser stripe centerlines from complex parts, significantly improving the extraction accuracy. Experimental results show that, after applying the MST and DFS methods, low-frequency noise is effectively removed, and the extraction results are highly consistent with manual methods. The Dice similarity coefficient approaches 1, and the maximum Hausdorff distance is only 3.3821 pixels. This method provides an efficient and reliable solution for the precise extraction of complex laser stripes and lays a solid data foundation for subsequent feature parameter calculation and 3D reconstruction.