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In this paper, we propose a normal estimation method for unstructured 3Dpoint clouds. In this method, a feature constraint mechanism called Local PlaneFeatures Constraint (LPFC) is used and then a multi-scale selection strategy isintroduced. The LPEC can be used in a single-scale point network architecturefor a more stable normal estimation of the unstructured 3D point clouds. Inparticular, it can partly overcome the influence of noise on a large samplingscale compared to the other methods which only use regression loss for normalestimation. For more details, a subnetwork is built after point-wise featuresextracted layers of the network and it gives more constraints to each point ofthe local patch via a binary classifier in the end. Then we use multi-taskoptimization to train the normal estimation and local plane classificationtasks simultaneously.Also, to integrate the advantages of multi-scale results,a scale selection strategy is adopted, which is a data-driven approach forselecting the optimal scale around each point and encourages subnetworkspecialization. Specifically, we employed a subnetwork called Scale EstimationNetwork to extract scale weight information from multi-scale features. Moreanalysis is given about the relations between noise levels, local boundary, andscales in the experiment. These relationships can be a better guide to choosingparticular scales for a particular model. Besides, the experimental resultshows that our network can distinguish the points on the fitting planeaccurately and this can be used to guide the normal estimation and ourmulti-scale method can improve the results well. Compared to somestate-of-the-art surface normal estimators, our method is robust to noise andcan achieve competitive results.

Normal Estimation for 3D Point Clouds via Local Plane Constraint and Multi-scale Selection