  In this paper, we propose a normal estimation method for unstructured 3D
point clouds. In this method, a feature constraint mechanism called Local Plane
Features Constraint (LPFC) is used and then a multi-scale selection strategy is
introduced. The LPEC can be used in a single-scale point network architecture
for a more stable normal estimation of the unstructured 3D point clouds. In
particular, it can partly overcome the influence of noise on a large sampling
scale compared to the other methods which only use regression loss for normal
estimation. For more details, a subnetwork is built after point-wise features
extracted layers of the network and it gives more constraints to each point of
the local patch via a binary classifier in the end. Then we use multi-task
optimization to train the normal estimation and local plane classification
tasks simultaneously.Also, to integrate the advantages of multi-scale results,
a scale selection strategy is adopted, which is a data-driven approach for
selecting the optimal scale around each point and encourages subnetwork
specialization. Specifically, we employed a subnetwork called Scale Estimation
Network to extract scale weight information from multi-scale features. More
analysis is given about the relations between noise levels, local boundary, and
scales in the experiment. These relationships can be a better guide to choosing
particular scales for a particular model. Besides, the experimental result
shows that our network can distinguish the points on the fitting plane
accurately and this can be used to guide the normal estimation and our
multi-scale method can improve the results well. Compared to some
state-of-the-art surface normal estimators, our method is robust to noise and
can achieve competitive results.
