From Virtuality To Reality: A Learningbased 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 indepth 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 scaleinvariant and effective on both rendering point cloud and point cloud of real scene.

LocalConvexity Reinforcement for Scene Reconstruction from Sparse Point Clouds Several methods reconstruct surfaces from sparse point clouds that are estimated from images. Most of them build 3D Delaunay triangulation of the points and compute occupancy labeling of the tetrahedra thanks to visibility information and surface constraints. However their most notable errors are falselylabeled freespace tetrahedra. We present labeling corrections of these errors based on a new shape constraint: localconvexity. In the simplest case, this means that a freespace tetrahedron of the Delaunay is relabeled matter if its size is small enough and all its vertices are in matter tetrahedra. The allowed corrections are more important in the vertical direction than in the horizontal ones to take into account the anisotropy of usual scenes. In the experiments, our corrections improve the results of previous surface reconstruction methods applied to videos taken by a consumer 360 camera.