From Virtuality To Reality: A Learning-based Point Cloud Labeling Method With Synthesis Scene

This paper proposes a machine learning based point cloud labeling algorithm. To classify point cloud in a sparse scan of both virtual and real scene as basic geometrical elements like planar and edge, a rendering dataset in virtual environment is created and labeled. Then the principal component analysis (PCA) is applied to calculate local geometrical features of point cloud. An in-depth analysis is performed by training several machine learning models with PCA features and experiments in which the trained models are applied to on both rendering point cloud and laser scan of real scene are conducted to validate that our approach is scale-invariant and effective on both rendering point cloud and point cloud of real scene.