###### Abstract  
Random speckle structured light can increase the texture information of the object surface, so it is added in the binocular stereo vision system to solve the matching ambiguity problem caused by the surface with repetitive pattern or no texture. To improve the reconstruction quality, many current researches utilize multiple speckle patterns for projection and use stereo matching methods based on spatiotemporal correlation. This paper presents a novel random speckle 3D reconstruction scheme, in which multiple speckle patterns are used and a weighted-fusion-based spatiotemporal matching cost function (STMCF) is proposed to find the corresponding points in speckle stereo image pairs. Furthermore, a parameter optimization method based on differential evolutionary (DE) algorithm is designed for automatically determining the values of all parameters included in STMCF. In this method, since there is no suitable training data with ground truth, we explore a training strategy where a passive stereo vision dataset with ground truth is used as training data and then apply the learned parameter value to the stereo matching of speckle stereo image pairs. Various experimental results verify that our scheme can realize accurate and high-quality 3D reconstruction efficiently and the proposed STMCF exhibits superior performance in terms of accuracy, computation time and reconstruction quality than the state-of-the-art method based on spatiotemporal correlation. random speckle structured light; binocular stereo vision; stereo matching; matching cost function; differential evolutionary algorithm +
Footnote †: journal: Medical Imaging
The active measurement methods usually emit optical signals to the surface of the object. Both time of flight (TOF) [11] and structured light [12] are typical active approaches. The TOF camera transmits continuous near-infrared ray to the target, then the reflected ray is received by a specific sensor. By measuring the flight time of the ray, the depth information is obtained. It has advantages of fast response and less computational cost. Nevertheless, it has the shortcomings of low accuracy and depth map resolution. The structured light technique also works on the principle of triangulation. Different with passive binocular stereo vision, it improves 3D reconstruction quality by projecting a pre-designed pattern onto the object. Fringe structured light [13] and random speckle structured light [14] are two mainstream structured light encoding methods currently. Compared with the former, random speckle structured light does not need accurate phase shift and the cost of projection equipment is relatively low. Hence, it is widely applied in various commercial 3D sensors, such as Kinect [4], RealSense [15], etc. As shown in Figure 1 (the photo in Figure 1 is a plastic human face mask, as are photos in Figure 2), random speckle structured light is commonly used in combination with the binocular stereo vision system because it can enrich texture information of the object surface that is helpful to find the matching point more accurately. In order to improve the reconstruction quality, many existing studies utilize multiple speckle patterns for projection and use stereo matching methods based on spatiotemporal correlation. In this paper, a novel random speckle 3D reconstruction scheme is presented. Sequence of different speckle patterns are projected to the measured object and then two cameras capture images from different views synchronously. The speckle pattern that we used is  
Figure 1: Schematic of binocular stereo vision system using random speckle structured light. Figure 2: Schematic diagram of the proposed random speckle 3D reconstruction scheme. designed by [16]. After acquiring sequence of speckle image pairs, the schematic diagram of the scheme is shown in Figure 2 and the main contributions are as follows:  
1. A weighted-fusion-based spatiotemporal matching cost function (STMCF) is proposed for accurately searching the matching point pairs between left and right speckle images. 2.