Abstract
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. The presented method uses Bezier patches to reconstruct missing data. The source of input data for the algorithm was an 8-directional structured light scanning system for the human body. Typical 3D scan representing human body consists of about 1 million points with average sampling density of 1 mm. 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. The developed algorithm was tested on simulated and scanned 3D input data. Quality assessment was made based on simulated cavities, reconstructed using presented method and compared to original 3D geometry. Additionally, comparison to the state-of-the-art screened Poisson method is presented. Values’ ranges of parameters influencing result of described method were estimated for sample scans and comprehensively discussed. The results of the quantitative assessment of the reconstruction were lower than 0,5 of average sampling density.
Highlights
MethodsMeshless methods were proposed byDavis et al [7] and by Chalmoviansky and Jüttler [3]
The approach described in [24] bases on the property of some scanning methods that cause the point cloud to be stored as horizontal scan lines sorted according to height
In order to approximate this value for the entire cloud, we introduce the average minimum distance (AMD)
Summary
Meshless methods were proposed byDavis et al [7] and by Chalmoviansky and Jüttler [3]. The first method [7] is based on defining a signed distance function, which describes the surface of the point cloud. The approach described in [24] bases on the property of some scanning methods that cause the point cloud to be stored as horizontal scan lines sorted according to height This enabled the authors to approximate discontinuities in scan lines using cubic spline curve approximation. Authors own that their method is more computationally expensive than state-of-the-art methods and is less robust to noise and outliers with irregular geometry Those disadvantages are less important for point cloud to mesh conversion, but become blocking issue in hole filling, where relatively big cavities with irregular and noisy edge may appear. Robustness Reconstructing to error on local shape measured area bounds Automatable
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