awesome papers of 3D objects detection and reconstruction
[1] H. A. Alhaija, S. K. Mustikovela, L. Mescheder, A. Geiger, and C. Rother. Augmented reality meets computer vision: Efficient data generation for urban driving scenes. International Journal of Computer Vision, 126(9):961–972, 2018. 5
[2] K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan. Unsupervised pixel-level domain adaptation with generative adversarial networks. CVPR, pages 95–104, 2017. 5
[3] X.Chen,K.Kundu,Z.Zhang,H.Ma,S.Fidler,andR.Urtasun.Monocular3dobjectdetectionforautonomousdriving.InTheIEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016. 1, 2, 5, 6, 7, 13
[4] X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler, and R. Urtasun. 3d object proposals for accurate object class detection. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, NIPS, pages 424–432, Cambridge, MA, USA, 2015. MIT Press. 2, 6
[5] X. Chen, H. Ma, J. Wan, B. Li, and T. Xia. Multi-view 3d object detection network for autonomous driving. In IEEE CVPR, 2017. 3
[6] M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele. The cityscapes dataset for semantic urban scene understanding. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2016. 6
[7] Z. Deng and L. J. Latecki. Amodal detection of 3d objects: Inferring 3d bounding boxes from 2d ones in rgb-depth images. In Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 2
[8] A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun. CARLA: An open urban driving simulator. In Proceedings of the 1st Annual Conference on Robot Learning, pages 1–16, 2017. 5
[9] D. Eigen, C. Puhrsch, and R. Fergus. Depth map prediction from a single image using a multi-scale deep network. In Advances in neural information processing systems, pages 2366–2374, 2014. 1
[10] A. Gaidon, Q. Wang, Y. Cabon, and E. Vig. Virtual worlds as proxy for multi-object tracking analysis. In CVPR, 2016. 5
[11] R.Garg,V.K.BG,G.Carneiro,andI.Reid.Unsupervisedcnnforsingleviewdepthestimation:Geometrytotherescue.InEuropean Conference on Computer Vision, pages 740–756. Springer, 2016. 1, 3
[12] A. Geiger, P. Lenz, and R. Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In Conference on Computer Vision and Pattern Recognition (CVPR), 2012. 2, 5, 14
[13] C. Godard, O. Mac Aodha, and G. J. Brostow. Unsupervised monocular depth estimation with left-right consistency. In CVPR,volume 2, page 7, 2017. 1
[14] A. Grabner, P. M. Roth, and V. Lepetit. 3D Pose Estimation and 3D Model Retrieval for Objects in the Wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018. 2
[15] K. He, G. Gkioxari, P. Dolla ́r, and R. Girshick. Mask R-CNN. In Proceedings of the InternationalConference on Computer Vision(ICCV), 2017. 2, 4
[16] 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 Asian Conference on Computer Vision, 2012. 2
[17] S. Hinterstoisser, V. Lepetit, P. Wohlhart, and K. Konolige. On pre-trained image features and synthetic images for deep learning. CoRR, abs/1710.10710, 2017. 5
[18] A. Kanazawa, S. Tulsiani, A. A. Efros, and J. Malik. Learning category-specific mesh reconstruction from image collections. In ECCV, 2018. 1, 2
[19] H.Kato,Y.Ushiku,andT.Harada.Neural3dmeshrenderer.InProceedingsoftheIEEEConferenceonComputerVisionandPattern Recognition, pages 3907–3916, 2018. 2
[20] W. Kehl, F. Manhardt, F. Tombari, S. Ilic, and N. Navab. Ssd-6d: Making rgb-based 3d detection and 6d pose estimation great again.In The IEEE International Conference on Computer Vision (ICCV), Oct 2017. 1, 2, 3, 5
[21] A. Kendall, Y. Gal, and R. Cipolla. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 7 14
[22] J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. Waslander. Joint 3d proposal generation and object detection from view aggregation. IROS, 2018. 3
[23] A. Kundu, Y. Li, and J. M. Rehg. 3d-rcnn: Instance-level 3d object reconstruction via render-and-compare. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018. 1, 2, 4
[24] Y. Li, G. Wang, X. Ji, Y. Xiang, and D. Fox. Deepim: Deep iterative matching for 6d pose estimation. In The European Conference on Computer Vision (ECCV), September 2018. 1, 2
[25] T.-Y.Lin,P.Dolla ́r,R.B.Girshick,K.He,B.Hariharan,andS.J.Belongie.Featurepyramidnetworksforobjectdetection.InCVPR, volume 1, page 4, 2017. 2
[26] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dolla ́r. Focal loss for dense object detection. IEEE transactions on pattern analysis and machine intelligence, 2018. 2
[27] R.Liu,J.Lehman,P.Molino,F.P.Such,E.Frank,A.Sergeev,andJ.Yosinski.Anintriguingfailingofconvolutionalneuralnetworks and the coordconv solution. CoRR, abs/1807.03247, 2018. 3
[28] W. E. Lorensen and H. E. Cline. Marching cubes: A high resolution 3d surface construction algorithm. In Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’87, pages 163–169, New York, NY, USA, 1987. ACM. 5
[29] R.Mahjourian,M.Wicke,andA.Angelova.Unsupervisedlearningofdepthandegomotionfrommonocularvideousing3dgeometric constraints. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018. 4
[30] F. Manhardt, W. Kehl, N. Navab, and F. Tombari. Deep model-based 6d pose refinement in rgb. In The European Conference on Computer Vision (ECCV), September 2018. 1, 2, 4
[31] A. Mousavian, D. Anguelov, J. Flynn, and J. Kosecka. 3d bounding box estimation using deep learning and geometry. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5632–5640, 2017. 1, 2, 3, 4
[32] A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer. Automatic differentiation in pytorch. In NIPS-W, 2017. 5
33] S. Pillai, R. Ambrus, and A. Gaidon. Superdepth: Self-supervised, super-resolved monocular depth estimation, 2018. 1, 3, 5
[34] M. Rad and V. Lepetit. Bb8: A scalable, accurate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth. In The IEEE International Conference on Computer Vision (ICCV), Oct 2017. 1, 2
[35] M. Rad, M. Oberweger, and V. Lepetit. Feature mapping for learning fast and accurate 3d pose inference from synthetic images. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 5
[36] S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. In Neural Information Processing Systems (NIPS), 2015. 2
[37] B. Tekin, S. N. Sinha, and P. Fua. Real-time seamless single shot 6d object pose prediction. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018. 2
[38] E. Weiszfeld. Sur le point pour lequel la somme des distances de n points donne ́s est minimum. Tohoku Mathematical Journal, First Series, 43:355–386, 1937. 4
[39] Y. Wu and K. He. Group normalization. In CVPR, 2018. 3
[40] Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox. Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes. Robotics: Science and Systems (RSS), 2018. 1, 2, 3
[41] B. Xu and Z. Chen. Multi-level fusion based 3d object detection from monocular images. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018. 2, 6
[42] T. Zhou, M. Brown, N. Snavely, and D. G. Lowe. Unsupervised learning of depth and ego-motion from video. In CVPR, volume 2, page 7, 2017. 1