The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Deep Parametric Continuous Convolutional Neural Networks
Open accessAbstract
Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks. This limits their applicability to many real-world applications. In this paper we propose Parametric Continuous Convolution, a new learnable operator that operates over non-grid structured data. The key idea is to exploit parameterized kernel functions that span the full continuous vector space. This generalization allows us to learn over arbitrary data structures as long as their support relationship is computable. Our experiments show significant improvement over the state-of-the-art in point cloud segmentation of indoor and outdoor scenes, and lidar motion estimation of driving scenes.
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Cites
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Self-citationThe graph neural network model
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Geometric Deep Learning: Going beyond Euclidean data
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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
Swin Transformer V2: Scaling Up Capacity and Resolution
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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
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Metadata
- Authors
- Wang, S., Suo, S., Ma, W.-C., Pokrovsky, A., & Urtasun, R.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 2589-2597
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2798297823
- Cited by
- 359
- References
- 15
- FWCI
- 56.18
- Type
- conference paper
- Language
- en
- Open access
- green
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