Learning-Based Hole Detection in 3D Point Cloud Towards Hole Filling

In this paper, we propose a learning-based approach for automatic detection of hole boundary points in a 3D point cloud. Point cloud is an important geometric data structure used in 3D modelling. Data obtained from automatic acquisition techniques often result in geometric deficiencies such as holes in the 3D point cloud. For successful hole-filling to achieve better surface reconstruction, accurate detection of hole boundary points in the point cloud is necessary. Most of the existing methods use threshold values for different parameters which need to be set manually after analyzing the nature of point cloud. It becomes difficult to generalize the threshold values for a wide variety of point clouds. To overcome this limitation, we propose a deep learning framework to detect holes in the point cloud. The architecture directly consumes point cloud and considers the permutation invariance of points. Each point in the point cloud is classified as a hole boundary point or not. The detected hole boundary points are used for hole filling by fitting a surface and interpolating points on the surface.