The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Pointwise Convolutional Neural Networks
Open accessAbstract
Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored. In this paper, we present a convolutional neural network for semantic segmentation and object recognition with 3D point clouds. At the core of our network is point-wise convolution, a new convolution operator that can be applied at each point of a point cloud. Our fully convolutional network design, while being surprisingly simple to implement, can yield competitive accuracy in both semantic segmentation and object recognition task.
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Cites
Deep Residual Learning for Image Recognition
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
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Cited by
KPConv: Flexible and Deformable Convolution for Point Clouds
Thomas, H., Qi, C. R., Deschaud, J.-E., Marcotegui, B., Goulette, F., & Guibas, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00651 · Cited by 3,212
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., & Gall, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00939 · Cited by 2,294
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., & Markham, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01112 · Cited by 2,275
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
PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds
Xu, M., Ding, R., Zhao, H., & Qi, X. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00319 · Cited by 523
Metadata
- Authors
- Hua, B.-S., Tran, M.-K., & Yeung, S.-K.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 984-993
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2795374598
- Cited by
- 615
- References
- 43
- FWCI
- 97.95
- Type
- conference paper
- Language
- en
- Open access
- green
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