# Dense Robust 3D Reconstruction and Measurement for 3D Printing Process Based on Vision
## 6 Conclusions  
The real-time detection of parts in the printing process is one of the keys to form the closed-loop control. Based on the vision 3D measurement theory, this paper proposes a high-precision and rapid 3D reconstruction method of 3D printing process based on vision, and designs the corresponding detection structure. In order to improve the speed of 3D reconstruction, the FFT-SIFT algorithm, which can realize the rapid construction of scale space, is integrated with the AKAZE algorithm to improve the speed of feature extraction and obtain more feature matching information at the same time. Combining the incremental SFM algorithm with the global SFM algorithm, an integrated SFM sparse point cloud reconstruction method is developed to improve the reconstruction efficiency and robustness. On this basis, point cloud densification, point cloud model meshwork and texture mapping are utilized to achieve accurate 3D reconstruction of 3D printing process model. The comparative experimental analysis shows that compared with the classical SIFT algorithm, the proposed 3D reconstruction method can improve the extraction speed of feature points by 25.0%, improved the number of feature matches by 72%, and the reconstruction relative error is about 0.014%, which is close to the theoretical error. The proposed 3D printing process detection method has good application value and industrial significance. In conclusion, the main advantages of the optimization method in this paper are: in the process of 3D reconstruction of 3D printed structures with single color and texture, the process of feature extraction and matching not only maintains the robustness of scale but also retains more edge details on the basis of improving the matching speed. At the same time, the proposed integrated SFM method not only maintains the reconstruction speed and accuracy, but also has good robustness in the process of multi views reconstruction. The three-dimensional reconstruction method in this paper can be widely used in the three-dimensional detection of small and medium-sized structural parts. In order to improve the practicability of the detection system, the following two technical and theoretical problems need to be discussed and improved in the follow-up work:  
Figure 14: Accuracy analysis of reconstruction results. The impact on the print results for lowering and rising the printing platform (print bed) during the printing process. In the process of 3D printing, image acquisition is a static process, so it is necessary to pause printing and take photos on the lifting platform. This process may bring some slight deviations along the \(z\)-axis. In order to improve the accuracy of the printing process, we can use better motors to drive the \(z\)-axis movement and optimize the mechanical structure of the printer to ensure the effect of 3D reconstruction. At the same time, in order to prevent the problem of complete cooling of the material caused by the long pause of printing, better cameras can be used to shorten the exposure time and improve the stability, so as to accelerate the process of taking photos. Besides, continuous experiments can be carried out to shorten the photographic interval, so as to reduce the negative impact of material temperature drop in the printing gap on the printing quality. Secondly, consider improving the structure of the detection system, so as to avoid the movement of the printing platform in the \(z\)-axis direction in the process of image acquisition. In order to realize dynamic online detection, multi vision synchronous image acquisition can be considered. In the 3D reconstruction of the actual 3D printing process, the reconstruction accuracy is greatly affected by the hardware platform. According to Equation (19), the main factors affecting the reconstruction accuracy are: the distance between the camera and the printing structure, the pixel of the camera, and the focal length of the camera.