## 1 Introduction  
With the development of science and technology, lithium batteries with excellent performance have been widely used in many fields. However, there may be various surface defects in the production process of lithium batteries, which can produce many safety risks [1]. Therefore, the detection of surface defects of lithium batteries is particularly important in the early stage [2]. At present, the detection methods of lithium batteries are mainly reflected in the following two aspects: on the one hand, most of the product lines use human eye observation [3], which is labor-intensive and has a high error rate. On the other hand, the traditional visual two-dimensional surface inspection is greatly interfered with by external factors such as external light [4], product deformation, information collection [5], and difficult program debugging [6]. Due to the environmental limitations of 3D information extraction in industry, compared with the direct detection of features, machine vision has attracted the attention of researchers. The 2D industrial machine vision cannot obtain the height information of the detected surface defects, so the point cloud information of the measured surface is collected through 3D machine vision to achieve 3D reconstruction [7]. [PERSON] et al. [8] proposed an improved edge scan extraction model based on CCD camera vibration frequency and established a 3D quantitative method for surface defects of cast plate embryos. [PERSON] et al. [9] established a 3D reconstruction system based on fringe projection technology to solve the surface detection problem of non-metallic pipelines transporting hydrocarbons. [PERSON] et al. [10] developed a surface detection system with 3D characteristic defects on the surface of flat steel using multi-spectral stereoscopic technology that could reliably detect small surface defects. A detection technology improved by [PERSON] et al. [11] can identify and classify defects in transparent substrates by using diffraction characteristics of digital holograms and machine learning algorithms. [PERSON] et al. [12] proposed an automatic defect detection technology combining laser line projection and stereo vision, which effectively reduced the influence of high-precision surfaces on the accuracy of 3D reconstruction. [PERSON] et al. [13] processed the data required for defect detection and 3D reconstruction based on laser scanning and adopted a BP neural network to filter the obtained point cloud data, effectively reducing the complexity of the 3D reconstruction of the weld surface. At present, research on the 3D reconstruction of surface defects mostly focuses on the identification of defects, while the quantitative analyses for defect classification and statistics are relatively few. Among many measurement methods for 3D reconstruction, laser measurement, as a non-contact measurement, does not damage the measured object and has high measurement accuracy and speed [14]. [PERSON] et al. [15] proposed a 3D surface reconstruction method based on triangulation, which can generate 3D surfaces. [PERSON] et al. [16] proposed a 3D point cloud detection method based on laser scanning and data low noise processing technology, which showed high accuracy for complex surfaces. The mobile laser scanning system built by [PERSON] et al. [17] can quickly and automatically reconstruct the 3D model of urban roads. [PERSON] et al. [18] improved the point cloud surface reconstruction algorithm of laser imaging radar to build a dense 3D depth surface, improving the robustness and reconstruction accuracy of the surface reconstruction algorithm. In order to solve the problem of moving objects, [PERSON] et al. [19] designed a 3D reconstruction algorithm based on line laser rotational scanning and established a vision measurement system of a rotating platform. [PERSON] et al. [20] used a 3D data registration based on black and white ring marking by using monoculture laser line scanning technology, and real sculpture experiments verified the effectiveness of the proposed method.