# The Automatic Algorithm of Optimizing the Position of Structured Light Sensors
Furthermore, they chose the more robust line-shifting code and binarization method for subpixel 3D reconstruction. [PERSON] et al. [10] proposed a method. The multi-intensity projection method was adopted by reducing the input projection intensity step-by-step and reconstructing the remaining pixels around the specular angle. Finally, the reconstruction result can be obtained by stitching point clouds at each projection intensity. Experiments verified that the proposed method could improve the integrity of reconstructed point clouds and measurement efficiency. [PERSON] et al. [11] proposed a stripe projection contouring method based on fully automated multiple exposures, which requires human intervention and greatly simplifies the whole reconstruction process. It is mathematically proven that once the modulation of a pixel is greater than a threshold value, the phase quality of that pixel can be considered satisfactory. This threshold can be used to guide the calculation of the required exposure time. The software then automatically adjusts the exposure time of the camera and captures the desired streak images. Using these captured images, a final reconstruction with a high dynamic range can be easily obtained. [PERSON] et al. [12] proposed an exposure fusion (EF)-based structured light method to accurately reconstruct a three-dimensional model of the object with a special reflective surface. A robust binary gray code is adopted as our structured light pattern. They fuse a group of images with different exposure times into a single image with a high dynamic range. With the help of the EF technique, captured images that have many overexposed and underexposed regions can be well exposed. The EF method, which has a simple operational procedure, is adopted in their work. Based on the EF method, precise 3D reconstruction of a reflective surface can be realized. [PERSON] et al. (2013) proposed a novel structured light approach for the 3D reconstruction of specular surfaces. The binary shifting strip is adopted as a structured light pattern instead of a conventional sinusoidal pattern. Based on the framework of conventional high-dynamic range imaging techniques, an efficient means is first introduced to estimate the camera response function. Then, the dynamic range of the generated radiance map is compressed in the gradient domain by introducing an attenuation function. Subject to the change in lighting conditions caused by projecting different structured light patterns, the structure light image with the middle exposure level is selected as the reference image and used for the slight adjustment of the primary fused image. Finally, the regenerated structured light images with well-exposing conditions are used for 3D reconstruction of the specular surface. To evaluate the performance of the method, some stainless steel stamping parts with strong reflectivity were used for the experiments. The results showed that different specular targets with various shapes can be precisely reconstructed by the proposed method. [PERSON] et al. (2014) designed a new algorithm based on reflective component separation (RCS) and priority region filling theory. The specular pixels in the image are first found by comparing the pixel parameters. Then, the reflection components are separated and processed. However, for objects such as ceramics and metals with strong specular highlights, the RCS theory will change the color information of the highlight pixels due to the larger specular reflection component. In this case, the preferred region filling theory is used to recover the color information. Schematic diagram of the principle of multi-angle measurement: the basic idea is to collect images of the same object from different angles so that the highlight areas of different images do not overlap, thus recovering data from the same scene.