## 1 Introduction  
Vision-based 3D reconstruction technology is an important element of research in the computer field [1]. It works mainly through the use of relevant instruments to obtain two-dimensional image data information for objects. The acquired data information is then analyzed and processed and, finally, the theory of 3D reconstruction is used to reconstruct the contour information for the object surface in the real environment [2, 3, 4, 5, 6, 7, 8, 9]. Vision-based 3D reconstruction technology has been widely used in the fields of unmanned vehicles [10], virtual reality [11], 3D printing [12], and engineering surveys [13]. At present, the 3D reconstruction methods for scenes are mainly divided into three categories [14]. The first type [15] involves the use of 3D laser scanning equipment to complete the 3D reconstruction, employing the optical principle to carry out optical scanning of the scene or object to obtain a high-accuracy 3D model. However, the reconstructed scene has no texture, and the laser scanning equipment is expensive. The second type is the 3D reconstruction approach represented by the structure from motion method [16]. It only needs a monocular camera and the reconstruction cost is low [17], but the image acquisition and calculation require significant amounts of time [18] and the real-time performance is not good. The third type [19, 20, 21, 22] is the 3D reconstruction method based on stereo vision. This method simulates the binocular imaging principle of human beings to obtain 3 Dinformation from the observed two-dimensional image information. It can be adapted to a variety of lighting environments; does not require too much human manipulation in use; can be employed for automatic, online, non-contact detection; has the advantages of adaptability, speed, high accuracy, low cost, etc. [6; 23; 24; 25; 26]; and can be successfully implemented for universal reconstruction projects. Although 3D reconstruction technology based on stereo vision has many advantages, the traditional method still has disadvantages, such as redundant calculation of stereo matching, low real-time processing speed, and the scene information selection directly affecting the reconstruction effect. For example, [PERSON] et al. [27] used an innovative MVS algorithm for the surface reconstruction task that employs planar patches of different scales to fit the surface and can yield a visualized 3D model with high accuracy. However, this method is built on the need for multiple views of the target to be reconstructed and a small target class, which makes it difficult to obtain better real-time processing and applicability. [PERSON] et al. [28] divided input stereo images into clusters with small overlaps to alleviate the scalability problem affecting large numbers of images in the feature-growth method, but their computational complexity remains the primary problem in scene reconstruction. [PERSON] [29] attempted to reconstruct the GMAW pool using a stereo-vision approach, but the significant effect of light intensity on the reconstruction quality suggested that the stereo reconstruction system could be improved. [PERSON] et al. [30]Substituting Equations (2) and (3) into Equation (1), the SFS Image irradiance equation can be obtained. \[I(x,y)=\rho(x,y)\frac{p_{s}p(x,y)+q_{s}q(x,y)+1}{\sqrt{p_{s}^{2}+q_{s}^{2}+1}\sqrt {p^{2}(x,y)+q^{2}(x,y)+1}} \tag{4}\]  
However, it is also clear that, for the image irradiance (Equation (4)), there are three unknowns: \(\rho(x,y)\), \(p(x,y)\), and \(q(x,y)\). One of the most straightforward approaches is to remove the issue by increasing the illumination constraints. In other words, in order to determine these three unknown quantities, at least three uncorrelated image irradiance equations need to be constructed, which is exactly the research idea proposed by [PERSON] [34], who developed the photometric stereo 3D reconstruction method.