  This paper presents a neural network for robust normal estimation on point
clouds, named AdaFit, that can deal with point clouds with noise and density
variations. Existing works use a network to learn point-wise weights for
weighted least squares surface fitting to estimate the normals, which has
difficulty in finding accurate normals in complex regions or containing noisy
points. By analyzing the step of weighted least squares surface fitting, we
find that it is hard to determine the polynomial order of the fitting surface
and the fitting surface is sensitive to outliers. To address these problems, we
propose a simple yet effective solution that adds an additional offset
prediction to improve the quality of normal estimation. Furthermore, in order
to take advantage of points from different neighborhood sizes, a novel Cascaded
Scale Aggregation layer is proposed to help the network predict more accurate
point-wise offsets and weights. Extensive experiments demonstrate that AdaFit
achieves state-of-the-art performance on both the synthetic PCPNet dataset and
the real-word SceneNN dataset.
