![]() The algorithm was developed as part of an 8-directional SLI scanning system designed at the Institute of Micromechanics and Photonics in Warsaw, Poland. The aim of the work presented in this paper was to develop an algorithm for filling cavities in surface 3D scans. These can all result in cavities of various shapes appearing in the scanned surfaces (Figs. In such cases, the visual effect is a major factor in determining the usability of a particular technique.įactors that cause discontinuity in scans of a continuous surface include self-occlusion of the scanned object, object movement during frame capture, or a limited number of directions from which the object is captured. ![]() In visualization systems, such as virtual fitting rooms, human perception is easily drawn to unnatural discontinuities in the models, and the effect of presenting a realistic object is lost. This makes creating a complete model time consuming, which in the case of in vivo measurements is undesirable and can lead to serious shape deformations. However, most of these techniques deliver data from a single point of view and are prone to self-occlusion. These methods use visible or near-to-visible light and are non-invasive, which has made them more popular in medicine, especially for examining children and pregnant women, where exposure to ionizing radiation should be strictly avoided. Methods that provide direct information about the surface, such as structured light illumination (SLI), laser triangulation, or Structure from Motion (SfM), are used much more frequently. These methods are costly, invasive, and generate excessive data hence, these methods are rarely used for surface capture. It is difficult to model such shapes realistically, not to mention obtaining a metrologically correct representation.Įxisting methods of capturing the surface of the human body include volumetric methods used primarily in medicine, such as magnetic resonance imaging (MRI) or computer tomography (CT), which allow the extraction of the whole surface from a single measurement. ![]() ![]() Especially significant applications of this technology are systems that use models of the human body, e.g., in medical, clothing and gaming industries. The results of the quantitative assessment of the reconstruction were lower than 0,5 of average sampling density.ģD scanning is becoming an increasingly important element of reality reconstruction, replacing manual modeling. Values’ ranges of parameters influencing result of described method were estimated for sample scans and comprehensively discussed. Additionally, comparison to the state-of-the-art screened Poisson method is presented. Quality assessment was made based on simulated cavities, reconstructed using presented method and compared to original 3D geometry. The developed algorithm was tested on simulated and scanned 3D input data. The paper describes the complete scan processing pipeline: data pre-processing, boundary selection, cavity extraction and reconstruction, and a post-processing step to smooth and resample resulting geometry. Typical 3D scan representing human body consists of about 1 million points with average sampling density of 1 mm. The source of input data for the algorithm was an 8-directional structured light scanning system for the human body. The presented method uses Bezier patches to reconstruct missing data. In this paper we introduce a cavity reconstructing algorithm for 3D surface scans (CRASS) developed for filling cavities in point clouds representing human body surfaces.
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