Point Cloud Completion By Minimizing Prediction Errors In Both 2D And 3D Spaces

Early works for 3D point cloud completion primarily focused on revising deep learning networks, and showed an acceptable performance. However, the performance those methods is limited because of lack of geometric studies. To solve that problem, we used multiple loss functions that allow the deep learning networks to fully understand the geometric perspective of the object in both two-dimensional (2D) and three-dimensional (3D) spaces. As a result, the angle between the prediction and ground truth vectors is reduced for accurate local details in the 3D space, and at the same time, the difference in distance on a specific plane is reduced for 2D local details. For that reason, the proposed point cloud completion method can lead to clarify semantic information and structure of 3D objects. In addition, the proposed mathematical approach can give better understanding of object, which can improve the performance of 3D point cloud completion.