3D Surface Reconstruction using Point Cloud Segmentation with Machine Learning

Abstract

3D point cloud is a kind of geometric data-structure, which is easily acquired using highly advanced 3D sensors. These sensing devices are capable of producing a considerable amount of 3D point clouds by capturing the 3D surface geometries and dimensions from targeted objects and real-world scenes. The captured 3D point clouds are utilized to reconstruct 3D objects, surfaces, shapes, and parts. 3D surface reconstruction is widely used in reverse engineering, 3D medical imaging, 3D printing, and automated medical surgery. Discrete surface reconstruction from 3D point clouds is still a challenging task, because captured data is un-ordered, noisy, redundant, and has topological imperfections. Due to these issues, it is not adequate for 3D surface reconstruction in its current form. In this direction, techniques based on geometric modeling have already been proposed to solve 3D surface reconstruction problems from point cloud data. Due to the irregular format of 3D point clouds, many researchers convert the data into 3D voxel grids or collection of images, which add unnecessary volume and cause issues. On the other hand, most of the deep learning-based techniques have focused on regular input data for surface reconstruction, and little attention has been paid towards 3D point cloud data. Therefore, to address the issues mentioned above, the proposed model is based on unified deep net architecture. The proposed deep net architecture takes raw 3D point clouds as an input. It performs a segmentation technique to tackle un-ordered data and further utilizes this segmentation information for 3D surface reconstruction of objects and their parts. To evaluate the performance of proposed model, experiments have been performed on the benchmark ShapeNet dataset. This dataset is a large-scale repository of 3D point clouds containing 16,880 objects of 16 different categories. The results show that the proposed method achieves superior results as compared to state-of-theart methods with an accuracy of 82% for 3D surface reconstruction