A Robust and Fast Reconstruction Framework for Noisy and Large Point Cloud Data

In this paper we present a robust reconstruction framework on noisy and large point cloud data. Though Poisson reconstruction performs well in recovering the surface from noisy point cloud data, it's problematic to reconstruct underlying surface from large cloud data, especially on a general processor. An inaccurate estimation of point normal for noisy and large dataset would result in local distortion on the reconstructed mesh. We adopt a systematical combination of Poisson-disk sampling, normal estimation and Poisson reconstruction to avoid the inaccuracy of normal calculated from k-nearest neighbors. With the fewer dataset obtained by sampling on original points, the normal estimated is more reliable for subsequent Poisson reconstruction and the time spent in normal estimation and reconstruction is much less. We demonstrate the effectiveness of the framework in recovering topology and geometry information when dealing with point cloud data from real world. The experiment results indicate that the framework is superior to Poisson reconstruction directly on raw point dataset in the aspects of time consumption and visual fidelity.