  We propose a surface fitting method for unstructured 3D point clouds. This
method, called DeepFit, incorporates a neural network to learn point-wise
weights for weighted least squares polynomial surface fitting. The learned
weights act as a soft selection for the neighborhood of surface points thus
avoiding the scale selection required of previous methods. To train the network
we propose a novel surface consistency loss that improves point weight
estimation. The method enables extracting normal vectors and other geometrical
properties, such as principal curvatures, the latter were not presented as
ground truth during training. We achieve state-of-the-art results on a
benchmark normal and curvature estimation dataset, demonstrate robustness to
noise, outliers and density variations, and show its application on noise
removal.
