Ed for the loss function , and its expression is as follows: ( x ) = x2 /2 2 x2 (15)Right after the worldwide position data of rotation and translation is obtained by the above methods, the twodimensional function points is often located into the threedimensional space by using triangle positioning. Ahead of processing the subsequent methods with incremental PNP, the similarity between the remaining pictures and other groups of pictures is evaluated, and these photos are sorted and processed in an effort to resolve the rotation and position relationship in between camera views. After the 3D point cloud obtained by international SFM, the major beam collimation algorithm is firstly carried out to optimize the 3D point cloud as well as the parameters of camera, after which the unprocessed pictures are successively added. The next step would be to use the PNP algorithm to resolve the parameters of camera and align them towards the existing model coordinate method after which get the 3D point cloud of this image by means of triangulation. Lastly, the new 3D point cloud is combined together with the fundamental point cloud, and the beam collimation system is applied for optimization when the new point cloud amount reaches a threshold. Just after all of the pictures are processed, the beam collimation system is implemented once again to acquire the final sparse point clouds. four.2. Dense Reconstruction of Point Cloud and 3D Reconstruction of Surface The SFM algorithm only gets a sparse threedimensional point cloud, which also has a massive range of voids inside, and also the specifics aren’t adequate, which cannot meet the reconstruction specifications of 3D printed components, and additional dense reconstruction is required. Patch based multi view stereo vision (PMVS) has the qualities of uncomplicated operation, much more correct reconstruction model, and can well deal with external point interference and occlusion . This paper utilizes this approach to get dense point clouds. However, in the actual application procedure, there is going to be a lot more discrete points, that will impact the final reconstruction impact. In order to get a far better effect, the traditional PMVS algorithm along with the statistical outlier removal function encapsulated by PCL point cloud library are made use of within this paper to take away the discrete outer points by way of statistical evaluation, so as to get a greater dense reconstruction effect. The principle is always to count the details of every single point and its surroundings, resolve the distance involving each and every point and its surroundings, and calculate the average worth. The result of the typical worth is expressed by Gaussian distribution as follows: D=i =di /nn(16)Appl. Sci. 2021, 11,11 ofwhere D is the typical distance with the Gaussian distribution, n is the total number of neighborhood points about the point, and di is definitely the distance amongst the neighborhood points as well as the point. As outlined by the threshold value set by D, the discrete outer points that don’t meet the needs is going to be removed to receive the dense point cloud with greater impact. Because Delaunay triangulation includes a sturdy adaptive potential and good antiimage noise Cirazoline GPCR/G Protein effect when it truly is extended to threedimensional space , Delaunay triangulation is applied to surface reconstruction just after acquiring dense point clouds in this paper, and for the realtime requirement of 3D printing, divide and conquer algorithm with low computational complexity is selected. The frequent Flumioxazin Autophagy texture map operator encapsulated in OpenGL library is applied to realize the texturization of mapping, restore and reconstruct the genuine texture.