The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Morphing and Sampling Network for Dense Point Cloud Completion
Open accessAbstract
3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community. For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods' results, we propose a novel approach to complete the partial point cloud in two stages. Specifically, in the first stage, the approach predicts a complete but coarse-grained point cloud with a collection of parametric surface elements. Then, in the second stage, it merges the coarse-grained prediction with the input point cloud by a novel sampling algorithm. Our method utilizes a joint loss function to guide the distribution of the points. Extensive experiments verify the effectiveness of our method and demonstrate that it outperforms the existing methods in both the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).
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Cites
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles, R. Q., Su, H., Kaichun, M., & Guibas, L. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.16 · Cited by 9,237
Dynamic Graph CNN for Learning on Point Clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199
Shortest Connection Networks And Some Generalizations
Prim, R. C. (1957) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1957.tb01515.x · Cited by 4,565
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Park, J. J., Florence, P., Straub, J., Newcombe, R., & Lovegrove, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00025 · Cited by 3,422
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00593 · Cited by 2,817
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
Fan, H., Su, H., & Guibas, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.264 · Cited by 2,402
Sinkhorn Distances: Lightspeed Computation of Optimal TransportPreprint
Cuturi, M. (2013) · Cited by 2,385
ShapeNet: An Information-Rich 3D Model Repository
Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al. (2015) · arXiv · doi:10.48550/arxiv.1512.03012 · Cited by 2,330
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02413 · Cited by 2,114
Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs
Landrieu, L., & Simonovsky, M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00479 · Cited by 1,543
Cited by
PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
Yu, X., Rao, Y., Wang, Z., Liu, Z., Lu, J., & Zhou, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01227 · Cited by 544
3D Shape Generation and Completion through Point-Voxel Diffusion
Zhou, L., Du, Y., & Wu, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00577 · Cited by 421
Metadata
- Authors
- Liu, M., Sheng, L., Yang, S., Shao, J., & Hu, S.-M.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 07
- Pages
- 11596-11603
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2997088169
- Cited by
- 334
- References
- 40
- FWCI
- 48.34
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Liu, M., Sheng, L., Yang, S., Shao, J., & Hu, S.-M. (2020). Morphing and Sampling Network for Dense Point Cloud Completion. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v34i07.6827
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