The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
PCPN et Learning Local Shape Properties from Raw Point Clouds
Open accessAbstract
In this paper, we propose PCPN ET, a deep‐learning based approach for estimating local 3D shape properties in point clouds. In contrast to the majority of prior techniques that concentrate on global or mid‐level attributes, e.g., for shape classification or semantic labeling, we suggest a patch‐based learning method, in which a series of local patches at multiple scales around each point is encoded in a structured manner. Our approach is especially well‐adapted for estimating local shape properties such as normals (both unoriented and oriented) and curvature from raw point clouds in the presence of strong noise and multi‐scale features. Our main contributions include both a novel multi‐scale variant of the recently proposed PointNet architecture with emphasis on local shape information, and a series of novel applications in which we demonstrate how learning from training data arising from well‐structured triangle meshes, and applying the trained model to noisy point clouds can produce superior results compared to specialized state‐of‐the‐art techniques. Finally, we demonstrate the utility of our approach in the context of shape reconstruction, by showing how it can be used to extract normal orientation information from point clouds.
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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
Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,613
Differential geometry of curves and surfaces
(2011) · Choice Reviews Online · doi:10.5860/choice.48-3330 · Cited by 3,706
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661
Surface reconstruction from unorganized points
Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., & Stuetzle, W. (1992) · Proceedings of the 19th annual conference on Computer graphics and interactive techniques · doi:10.1145/133994.134011 · Cited by 2,730
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
Volumetric and Multi-view CNNs for Object Classification on 3D Data
Qi, C. R., Su, H., NieBner, M., Dai, A., Yan, M., & Guibas, L. J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.609 · Cited by 1,504
Deep Convolutional Networks on Graph-Structured Data
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Surface reconstruction from unorganized points
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Cited by
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
Deep Hough Voting for 3D Object Detection in Point Clouds
Qi, C. R., Litany, O., He, K., & Guibas, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00937 · Cited by 1,244
MeshCNN: a network with an edge
Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., & Cohen-Or, D. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3322959 · Cited by 742
PU-Net: Point Cloud Upsampling Network
Yu, L., Li, X., Fu, C.-W., Cohen-Or, D., & Heng, P.-A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00295 · Cited by 697
Where2Act: From Pixels to Actions for Articulated 3D Objects
Mo, K., Guibas, L., Mukadam, M., Gupta, A., & Tulsiani, S. (2021) · arXiv · doi:10.48550/arxiv.2101.02692 · Cited by 132
Metadata
- Authors
- Guerrero, P., Kleiman, Y., Ovsjanikov, M., & Mitra, N. J.
- Venue
- Computer Graphics Forum
- Publisher
- Wiley
- Published
- 2018-05
- Volume
- 37
- Issue
- 2
- Pages
- 75-85
- ISSN
- 0167-7055 (ISSN-L)
- 1467-8659
- OpenAlex
- W2766448241
- Cited by
- 369
- References
- 36
- FWCI
- 28.18
- Type
- journal article
- Language
- en
- Open access
- green
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- Guerrero, P., Kleiman, Y., Ovsjanikov, M., & Mitra, N. J. (2018). PCPN et Learning Local Shape Properties from Raw Point Clouds. Computer Graphics Forum. https://doi.org/10.1111/cgf.13343
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