Affiliation:
1. School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Abstract
Point cloud completion aims to generate high-resolution point clouds using incomplete point clouds as input and is the foundational task for many 3D visual applications. However, most existing methods suffer from issues related to rough localized structures. In this paper, we attribute these problems to the lack of attention to local details in the global optimization methods used for the task. Thus, we propose a new model, called PA-NET, to guide the network to pay more attention to local structures. Specifically, we first use textual embedding to assist in training a robust point assignment network, enabling the transformation of global optimization into the co-optimization of local and global aspects. Then, we design a novel plug-in module using the assignment network and introduce a new loss function to guide the network’s attention towards local structures. Numerous experiments were conducted, and the quantitative results demonstrate that our method achieves novel performance on different datasets. Additionally, the visualization results show that our method efficiently resolves the issue of poor local structures in the generated point cloud.
Funder
National Natural Science Foundation of China
Subject
General Physics and Astronomy
Reference35 articles.
1. Wen, X., Li, T., Han, Z., and Liu, Y.S. (2020, January 13–19). Point cloud completion by skip-attention network with hierarchical folding. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.
2. A robust hole-filling algorithm for triangular mesh;Zhao;Vis. Comput.,2007
3. Sorkine, O., and Cohen-Or, D. (2004, January 7–9). Least-squares meshes. Proceedings of the Shape Modeling Applications, Genova, Italy.
4. Han, X., Li, Z., Huang, H., Kalogerakis, E., and Yu, Y. (2017, January 22–29). High-resolution shape completion using deep neural networks for global structure and local geometry inference. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.
5. Litany, O., Bronstein, A., Bronstein, M., and Makadia, A. (2018, January 18–23). Deformable shape completion with graph convolutional autoencoders. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.