  With the burst development of neural networks in recent years, the task of
normal estimation has once again become a concern. By introducing the neural
networks to classic methods based on problem-specific knowledge, the
adaptability of the normal estimation algorithm to noise and scale has been
greatly improved. However, the compatibility between neural networks and the
traditional methods has not been considered. Similar to the principle of
Occam's razor, that is, the simpler is better. We observe that a more
simplified process of surface fitting can significantly improve the accuracy of
the normal estimation. In this paper, two simple-yet-effective strategies are
proposed to address the compatibility between the neural networks and surface
fitting process to improve normal estimation. Firstly, a dynamic top-k
selection strategy is introduced to better focus on the most critical points of
a given patch, and the points selected by our learning method tend to fit a
surface by way of a simple tangent plane, which can dramatically improve the
normal estimation results of patches with sharp corners or complex patterns.
Then, we propose a point update strategy before local surface fitting, which
smooths the sharp boundary of the patch to simplify the surface fitting
process, significantly reducing the fitting distortion and improving the
accuracy of the predicted point normal. The experiments analyze the
effectiveness of our proposed strategies and demonstrate that our method
achieves SOTA results with the advantage of higher estimation accuracy over
most existed approaches.
