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C2FNet: A Coarse-to-Fine Network for Multi-View 3D Point Cloud Generation

Journal Article


Abstract


  • Generation of a 3D model of an object from multiple views has a wide range of applications. Different parts of an object would be accurately captured by a particular view or a subset of views in the case of multiple views. In this paper, a novel coarse-to-fine network (C2FNet) is proposed for 3D point cloud generation from multiple views. C2FNet generates subsets of 3D points that are best captured by individual views with the support of other views in a coarse-to-fine way, and then fuses these subsets of 3D points to a whole point cloud. It consists of a coarse generation module where coarse point clouds are constructed from multiple views by exploring the cross-view spatial relations, and a fine generation module where the coarse point cloud features are refined under the guidance of global consistency in appearance and context. Extensive experiments on the benchmark datasets have demonstrated that the proposed method outperforms the state-of-the-art methods.

Publication Date


  • 2022

Citation


  • Lei, J., Song, J., Peng, B., Li, W., Pan, Z., & Huang, Q. (2022). C2FNet: A Coarse-to-Fine Network for Multi-View 3D Point Cloud Generation. IEEE Transactions on Image Processing, 31, 6707-6718. doi:10.1109/TIP.2022.3203213

Scopus Eid


  • 2-s2.0-85140798737

Start Page


  • 6707

End Page


  • 6718

Volume


  • 31

Issue


Place Of Publication


Abstract


  • Generation of a 3D model of an object from multiple views has a wide range of applications. Different parts of an object would be accurately captured by a particular view or a subset of views in the case of multiple views. In this paper, a novel coarse-to-fine network (C2FNet) is proposed for 3D point cloud generation from multiple views. C2FNet generates subsets of 3D points that are best captured by individual views with the support of other views in a coarse-to-fine way, and then fuses these subsets of 3D points to a whole point cloud. It consists of a coarse generation module where coarse point clouds are constructed from multiple views by exploring the cross-view spatial relations, and a fine generation module where the coarse point cloud features are refined under the guidance of global consistency in appearance and context. Extensive experiments on the benchmark datasets have demonstrated that the proposed method outperforms the state-of-the-art methods.

Publication Date


  • 2022

Citation


  • Lei, J., Song, J., Peng, B., Li, W., Pan, Z., & Huang, Q. (2022). C2FNet: A Coarse-to-Fine Network for Multi-View 3D Point Cloud Generation. IEEE Transactions on Image Processing, 31, 6707-6718. doi:10.1109/TIP.2022.3203213

Scopus Eid


  • 2-s2.0-85140798737

Start Page


  • 6707

End Page


  • 6718

Volume


  • 31

Issue


Place Of Publication