  In this paper, we propose a normal estimation method for unstructured 3D
point clouds. This method, called Nesti-Net, builds on a new local point cloud
representation which consists of multi-scale point statistics (MuPS), estimated
on a local coarse Gaussian grid. This representation is a suitable input to a
CNN architecture. The normals are estimated using a mixture-of-experts (MoE)
architecture, which relies on a data-driven approach for selecting the optimal
scale around each point and encourages sub-network specialization. Interesting
insights into the network's resource distribution are provided. The scale
prediction significantly improves robustness to different noise levels, point
density variations and different levels of detail. We achieve state-of-the-art
results on a benchmark synthetic dataset and present qualitative results on
real scanned scenes.
