PG-Net:3D point cloud completion based on graph convolutional network

Abstract

With the advancement of autonomous driving technology, the problem of 3D point cloud completion has become increasingly important. Completing 3D point clouds can improve the accuracy of 3D object detection, which is crucial for the development of autonomous driving and other related fields. In this paper, we propose a new approach for 3D point cloud completion tasks using point cloud representation. We focuses on the point cloud completion problem using Graph Neural Network methods, which are known for their ability to capture topological features. Our approach utilizes key components extraction and learning from the point cloud, to constrain the output of the decoder, and thus enhance the performance of point cloud completion task. Our approach is able to overcome the limitations of traditional methods, such as memory consumption and computational burden, as well as the loss of detailed information caused by quantization operation in some sparse representation based methods. We conduct extensive experiments on several benchmark datasets to evaluate the performance of our approach and compare it to existing methods. Our experimental results demonstrate that our proposed method is competitive, achieving comparable or even better results compared to state-of-the-art models. In particular, we show that our method is able to improve upon the performance of earlier models and achieve results that are comparable to current state-of-the-art models. These results indicate that our approach is a promising solution for 3D point cloud completion tasks.