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This article describes the 3D handheld profiling system composed of a stereo camera and an illumination projector to collect high-resolution data for close range of applications. Visual navigation approach is either based on feature matching or on accurate target, and the target-based approach was found to be more accurate if the 3D object has less texture on its surface. Block matching algorithm was used to render the single-view 3D reconstruction. For multiview 3D modeling, coarse registration and final refinement of the point clouds using iterative closest point algorithm were utilized. The proposed approach yields good accuracy for multiview registration as demonstrated in the results of this research.

Abstract. This paper describes the implementation of a 3D handheld scanning system based on visual inertial pose estimation and structured light technique.3D scanning system is composed of stereo camera, inertial navigation system (INS) and illumination projector to collect high resolution data for close range applications. The proposed algorithm for visual pose estimation is either based on feature matching or using accurate target object. The integration of INS enables the scanning system to provide the fast and reliable pose estimation supporting visual pose estimates. Block matching algorithm was used to render two view 3D reconstruction. For multiview 3D approach, rough registration and final alignment of point clouds using iterative closest point algorithm further improves the scanning accuracy. The proposed system is potentially advantageous for the generation of 3D models in bio-medical applications.

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In this article, a multi-view registration approach for the 3D handheld profiling system based on the multiple shot structured light technique is proposed. The multi-view registration approach is categorized into coarse registration and point cloud refinement using the iterative closest point (ICP) algorithm. Coarse registration of multiple point clouds was performed using relative orientation and translation parameters estimated via homography-based visual navigation. The proposed system was evaluated using an artificial human skull and a paper box object. For the quantitative evaluation of the accuracy of a single 3D scan, a paper box was reconstructed, and the mean errors in its height and breadth were found to be 9.4 μm and 23 μm, respectively. A comprehensive quantitative evaluation and comparison of proposed algorithm was performed with other variants of ICP. The root mean square error for the ICP algorithm to register a pair of point clouds of the skull object was also found to be less than 1 mm.

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