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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 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.