In a structured light-based 3D scanning system, the overall 3D information of to-be-measured objects cannot be retrieved at one time automatically. Currently the 3D registration algorithms can be divided into the auxiliary objects-based method and the feature points-based method. The former requires extra calibration objects or positioning platforms, which limits its application in free-form 3D scanning task. The latter can be conducted automatically, however, most of them tried to recover the motion matrix from extracted 2D features, which has been proved to be inaccurate. This paper proposed an automatic and accurate full-view registration method for 3D scanning system. Instead of using the 3D information of detected feature points to estimate the coarse motion matrix, 3D points reconstructed by the 3D scanning system were utilized. Firstly, robust SIFT features were extracted from each image and corresponding matching point pairs are achieved from two adjacent left images. Secondly, re-project all of the 3D point clouds onto the image plane of each left camera and corresponding 2D image points can be obtained. Filter out correct matching points from all 2D reprojection points under the guidance of the extracted SIFT matching points. Then, the covariance method was adopted to estimate the coarse registration matrix of adjacent positions. This procedure was repeated among every adjacent viewing position of the 3D scanning system. Lastly, fast ICP algorithm was performed to conduct fine registration of multi-view point clouds. Experiments conducted on real data have verified the effectiveness and accuracy of the proposed method.
3D measurement of underwater targets could recover 3D morphology of objects/scenes in water, which has extensive application prospects in the fields of submarine map drawing, underwater resource exploration, and marine archaeology et al. 3D reconstruction based on stereoscopic vision is playing a more and more important role in the field of measurement due to its incomparable advantages, such as high automation, rapid accuracy and non-contact. However, its application in underwater target detection is limited by the complex underwater environment, the absorption and scattering of light in the water and so on, which will seriously affect the quality of the image collection. In this paper, a 3D reconstruction method of underwater target based on multi view stereo vision technology was studied. A 3D profilometry system which works underwater was set up. The collection of multi-view image data is completed by a single camera and a rotating device. Firstly, camera’s back projection model is used to calibrate the motion and parameters of the underwater vision system. Secondly, the underwater target is fixed on the rotating device, and a series of images under different viewpoints are collected. Then, feature detection and matching were carried out, and dense surface point clouds were generated by several steps of expansion and filtering operations. Finally, based on the generated dense point cloud, the 3D geometric mesh model of the target is obtained by using the Poisson reconstruction method. Color and texture are fused into the 3D mesh model to get the target with high fidelity.