The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
SphereNet: Learning Spherical Representations for Detection and Classification in Omnidirectional Images
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
(2016) · Cited by 31,590
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
Self-citationDeformable Convolutional Networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513
Advances in Neural Information Processing Systems 27 (NIPS 2014)
Zhong, M., Goddard, N., & Sutton, C. A. (2014) · Cited by 2,440
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Defferrard, M., Bresson, X., & Vandergheynst, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.09375 · Cited by 1,673
Invariant Scattering Convolution Networks
Bruna, J., & Mallat, S. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.230 · Cited by 1,633
Distributing many points on a sphere
Saff, E. B., & Kuijlaars, A. B. J. (1997) · The Mathematical Intelligencer · doi:10.1007/bf03024331 · Cited by 1,086
Cited by
Encoder–decoder network for pixel‐level road crack detection in black‐box images
Bang, S., Park, S., Kim, H., & Kim, H. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12440 · Cited by 423
Weyn, J. A., Durran, D. R., & Caruana, R. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002109 · Cited by 336
WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving
Yogamani, S., Hughes, C., Horgan, J., Sistu, G., Chennupati, S., Uricar, M., Milz, S., Simon, M., Amende, K., Witt, C., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00940 · Cited by 334
“Some examples of the few datasets that have fisheye are: Visual SLAM ground truth for indoor scenes with omnidirectional cameras in [7], SphereNet [9] containing 1200 labelled images of parked cars using 360° cameras (not strictly fisheye) and, in automotive, the Oxford Robotcar”
MethodPerraudin, N., Defferrard, M., Kacprzak, T., & Sgier, R. (2019) · Astronomy and Computing · doi:10.1016/j.ascom.2019.03.004 · Cited by 184
“A first approach, explored by [20] for molecular modeling and [21, 22] for omnidirectional imaging, is to use a 2D CNN on a discretisation of the sphere that is a grid, such as the equirectangular projection (Figure 6, left panel), or a set of grids, such as the cubed-sphere defined by [47] (Figure…”
BackgroundMethodInfluential
Metadata
- Authors
- Coors, B., Condurache, A. P., & Geiger, A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 525-541
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2895696451
- Cited by
- 353
- References
- 30
- FWCI
- 10.31
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Coors, B., Condurache, A. P., & Geiger, A. (2018). SphereNet: Learning Spherical Representations for Detection and Classification in Omnidirectional Images. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-01240-3_32
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
