  Learning implicit surface directly from raw data recently has become a very
attractive representation method for 3D reconstruction tasks due to its
excellent performance. However, as the raw data quality deteriorates, the
implicit functions often lead to unsatisfactory reconstruction results. To this
end, we propose a novel edge-preserving implicit surface reconstruction method,
which mainly consists of a differentiable Laplican regularizer and a dynamic
edge sampling strategy. Among them, the differential Laplican regularizer can
effectively alleviate the implicit surface unsmoothness caused by the point
cloud quality deteriorates; Meanwhile, in order to reduce the excessive
smoothing at the edge regions of implicit suface, we proposed a dynamic edge
extract strategy for sampling near the sharp edge of point cloud, which can
effectively avoid the Laplacian regularizer from smoothing all regions.
Finally, we combine them with a simple regularization term for robust implicit
surface reconstruction. Compared with the state-of-the-art methods,
experimental results show that our method significantly improves the quality of
3D reconstruction results. Moreover, we demonstrate through several experiments
that our method can be conveniently and effectively applied to some point cloud
analysis tasks, including point cloud edge feature extraction, normal
estimation,etc.
