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Joint Austrian Computer Vision and Robotics Workshop 2020
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ACKNOWLEDGMENT Thisworkhasbeensupportedby theAustrianRe- searchPromotionAgency in theprogramProduction of the Future funded project MMAssist II (FFG No. 858623), the Austrian Ministry for Transport, Inno- vationandTechnology(bmvit) and theAustrianSci- ence Foundation (FWF) under grant agreement No. I3969-N30 (InDex). References [1] A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. Van Der Smagt, D. Cre- mers, and T. Brox. Flownet: Learning optical flow with convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages2758–2766,2015. [2] K.He,G.Gkioxari,P.Dolla´r,andR.Girshick. Mask r-cnn. InProceedingsof theIEEEinternationalcon- ferenceoncomputervision,pages2961–2969,2017. [3] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages770–778,2016. [4] S. Hinterstoisser, V. Lepetit, S. Ilic, S. Holzer, G. Bradski, K. Konolige, and N. Navab. Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. In Proceedings of Asian conference on computer vi- sion, pages548–562,2012. [5] T. Hodan, F. Michel, E. Brachmann, W. Kehl, A. GlentBuch, D. Kraft, B. Drost, J. Vidal, S. Ihrke, X.Zabulis,etal. Bop: Benchmarkfor6dobjectpose estimation. In Proceedings of the European Con- ference on Computer Vision (ECCV), pages 19–34, 2018. [6] M. Jaderberg, K. Simonyan, A. Zisserman, et al. Spatial transformernetworks. InAdvances inneural information processing systems, pages 2017–2025, 2015. [7] W. Kehl, F. Manhardt, F. Tombari, S. Ilic, and N. Navab. SSD-6D: making rgb-based 3d detec- tion and 6d pose estimation great again. CoRR, abs/1711.10006,2017. [8] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Im- agenet classification with deep convolutional neural networks. In Advances in neural information pro- cessingsystems, pages 1097–1105,2012. [9] K. Lenc and A. Vedaldi. Understanding image rep- resentations by measuring their equivariance and equivalence. InProceedingsof the IEEEconference on computer vision and pattern recognition, pages 991–999,2015. [10] Y.Li,G.Wang,X.Ji,Y.Xiang,andD.Fox.Deepim: Deep iterative matching for 6d pose estimation. In Proceedings of the European Conference on Com- puterVision (ECCV), pages 683–698,2018. [11] C.-H. Lin, E. Yumer, O. Wang, E. Shechtman, and S. Lucey. St-gan: Spatial transformer generative adversarial networks for image compositing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 9455–9464, 2018. [12] F. Manhardt, W. Kehl, N. Navab, and F. Tombari. Deepmodel-based6dposerefinementinrgb. InPro- ceedings of the European Conference on Computer Vision (ECCV), pages800–815,2018. [13] M. Oberweger, M. Rad, and V. Lepetit. Making deep heatmaps robust to partial occlusions for 3d object pose estimation. In Proceedings of the Eu- ropean Conference on Computer Vision (ECCV), pages 119–134,2018. [14] C. R. Qi, H. Su, K. Mo, and L. J. Guibas. Point- net: Deep learningonpoint sets for3dclassification and segmentation. In Proceedings of the IEEE con- ference on computer vision and pattern recognition, pages 652–660,2017. [15] C.R.Qi,L.Yi,H.Su,andL.J.Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in neural information processing systems, pages 5099–5108,2017. [16] M. Rad and V. Lepetit. Bb8: A scalable, accurate, robust topartial occlusionmethod forpredicting the 3dposesofchallengingobjectswithoutusingdepth. In Proceedings of the IEEE International Confer- ence onComputerVision, pages3828–3836,2017. [17] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. Imagenet large scale visual recognition challenge. International Journal ofComputer Vision, 115:211–252, 2015. [18] M. Sundermeyer, Z.-C. Marton, M. Durner, M. Brucker, and R. Triebel. Implicit 3d orientation learning for6dobjectdetection from rgb images. In Proceedings of the European Conference on Com- puterVision (ECCV), pages 699–715,2018. [19] D. E. Worrall, S. J. Garbin, D. Turmukhambetov, and G. J. Brostow. Interpretable transformations with encoder-decoder networks. In Proceedings of the IEEEInternationalConferenceonComputerVi- sion, pages5726–5735,2017. [20] Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox. Posecnn: Aconvolutionalneuralnetwork for6dob- jectposeestimation inclutteredscenes. 2018. [21] S.Zakharov, I.Shugurov,andS. Ilic. Dpod: 6dpose object detector and refiner. In Proceedings of the IEEEInternationalConferenceonComputerVision, pages 1941–1950,2019. 113
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Joint Austrian Computer Vision and Robotics Workshop 2020
Titel
Joint Austrian Computer Vision and Robotics Workshop 2020
Herausgeber
Graz University of Technology
Ort
Graz
Datum
2020
Sprache
englisch
Lizenz
CC BY 4.0
ISBN
978-3-85125-752-6
Abmessungen
21.0 x 29.7 cm
Seiten
188
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Informatik
Technik
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Joint Austrian Computer Vision and Robotics Workshop 2020