The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
BOP: Benchmark for 6D Object Pose Estimation
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
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
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Scharstein, D., & Szeliski, R. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014573219977 · Cited by 6,740
KinectFusion: Real-time dense surface mapping and tracking
Newcombe, R. A., Fitzgibbon, A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A. J., Kohi, P., Shotton, J., & Hodges, S. (2011) · 2011 10th IEEE International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2011.6092378 · Cited by 4,008
Using spin images for efficient object recognition in cluttered 3D scenes
Johnson, A. E., & Hebert, M. (1999) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.765655 · Cited by 2,599
Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., & Navab, N. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-37331-2_42 · Cited by 1,161
SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again
Kehl, W., Manhardt, F., Tombari, F., Ilic, S., & Navab, N. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.169 · Cited by 1,047
Self-citationSHOT: Unique signatures of histograms for surface and texture description
Salti, S., Tombari, F., & Di Stefano, L. (2014) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2014.04.011 · Cited by 987
Self-citation
Cited by
Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation
Park, K., Patten, T., & Vincze, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00776 · Cited by 455
iNeRF: Inverting Neural Radiance Fields for Pose Estimation
Yen-Chen, L., Florence, P., Barron, J. T., Rodriguez, A., Isola, P., & Lin, T.-Y. (2021) · 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636708 · Cited by 404
BOP Challenge 2020 on 6D Object Localization
Hodaň, T., Sundermeyer, M., Drost, B., Labbé, Y., Brachmann, E., Michel, F., Rother, C., & Matas, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-66096-3_39 · Cited by 268
Self-citationDeep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
Proenca, P. F., & Gao, Y. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9197244 · Cited by 229
“In their benchmark, [26] concluded that learningbased solutions are still not on par with point-cloud-based methods [27] in terms of precision.”
BackgroundMethodSatellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement
Chen, B., Cao, J., Parra, A., & Chin, T.-J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00343 · Cited by 203
Metadata
- Authors
- Hodaň, T., Michel, F., Brachmann, E., Kehl, W., Buch, A. G., Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., Sahin, C., Manhardt, F., Tombari, F., Kim, T.-K., Matas, J., & Rother, C.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 19-35
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2888752296
- Cited by
- 470
- References
- 31
- FWCI
- 60.59
- Type
- conference paper
- Language
- en
- Open access
- green
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- Hodaň, T., Michel, F., Brachmann, E., Kehl, W., Buch, A. G., Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., Sahin, C., Manhardt, F., Tombari, F., Kim, T.-K., Matas, J., & Rother, C. (2018). BOP: Benchmark for 6D Object Pose Estimation. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-01249-6_2
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