Low-density point eating algorithm for surface reconstruction from dense scans

We present a low-density point eating algorithm for surface reconstruction from dense scans. First, the density map for each scan is estimated and the boundary densities are down-weighted. Subsequently, the poorly scanned low-density overlapping points are eaten up based on a user-specified threshol...

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Bibliographic Details
Published in:Applied optics (2004) 2018-03, Vol.57 (8), p.1887
Main Authors: Shi, Bao-Quan, Feng, Xiao-Yuan, Zhang, Li-Kun, Yao, Chen-Song, Ye, Jun-Jie
Format: Article
Language:English
Subjects:
Algorithms
Density
Ground truth
Reconstruction
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Summary:We present a low-density point eating algorithm for surface reconstruction from dense scans. First, the density map for each scan is estimated and the boundary densities are down-weighted. Subsequently, the poorly scanned low-density overlapping points are eaten up based on a user-specified threshold. Finally, the overlapping areas are thinned by using the moving least-squares operator and the homogeneous points are weighted averaged. The new algorithm can extract smooth but detailed point set surfaces that are as close as possible to the ground truth. The good performance of the new algorithm is demonstrated by comparison with several advanced surface reconstruction algorithms.
ISSN:1559-128X
2155-3165
DOI:10.1364/AO.57.001887