The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Tangent Convolutions for Dense Prediction in 3D
Open accessAbstract
We present an approach to semantic scene analysis using deep convolutional networks. Our approach is based on tangent convolutions - a new construction for convolutional networks on 3D data. In contrast to volumetric approaches, our method operates directly on surface geometry. Crucially, the construction is applicable to unstructured point clouds and other noisy real-world data. We show that tangent convolutions can be evaluated efficiently on large-scale point clouds with millions of points. Using tangent convolutions, we design a deep fully-convolutional network for semantic segmentation of 3D point clouds, and apply it to challenging real-world datasets of indoor and outdoor 3D environments. Experimental results show that the presented approach outperforms other recent deep network constructions in detailed analysis of large 3D scenes.
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RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
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Guo, M.-H., Cai, J.-X., Liu, Z.-N., Mu, T.-J., Martin, R. R., & Hu, S.-M. (2021) · Computational Visual Media · doi:10.1007/s41095-021-0229-5 · Cited by 2,100
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
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Metadata
- Authors
- Tatarchenko, M., Park, J., Koltun, V., & Zhou, Q.-Y.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 3887-3896
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2797997528
- Cited by
- 593
- References
- 57
- FWCI
- 93.88
- Type
- conference paper
- Language
- en
- Open access
- green
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