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Depth estimation is a core task in 3D computer vision.
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International journal of computer vision 47
2002
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Atkinson, G.A., Hancock, E.R.: Recovery of surface orientation from diffuse polarization. IEEE transactions on image processing 15
2006
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Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: A. Fitzgibbon et al. (Eds.) (ed.) European Conf. on Computer Vision (ECCV). pp. 611–625. Part IV, LNCS 7577, Springer-Verlag (Oct 2012)
2012
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Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE conference on computer vision and pattern recognition. pp. 3354–3361. IEEE (2012)
2012
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Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from rgbd images. In: European conference on computer vision. pp. 746–760. Springer (2012)
2012
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Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of rgb-d slam systems. In: 2012 IEEE/RSJ international conference on intelligent robots and systems. pp. 573–580. IEEE (2012)
2012
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Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: The kitti dataset. The International Journal of Robotics Research 32
2013
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Xiao, J., Owens, A., Torralba, A.: Sun3d: A database of big spaces reconstructed using sfm and object labels. In: Proceedings of the IEEE international conference on computer vision. pp. 1625–1632 (2013)
2013
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Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. In: Advances in neural information processing systems. pp. 2366–2374 (2014)
2014
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2014
Earlier work this paper cites.
Scharstein, D., Hirschmüller, H., Kitajima, Y., Krathwohl, G., Nešić, N., Wang, X., Westling, P.: High-resolution stereo datasets with subpixel-accurate ground truth. In: German conference on pattern recognition. pp. 31–42. Springer (2014)
2014
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Garcia, N.M., De Erausquin, I., Edmiston, C., Gruev, V.: Surface normal reconstruction using circularly polarized light. Optics express 23
2015
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Garg, R., Bg, V.K., Carneiro, G., Reid, I.: Unsupervised cnn for single view depth estimation: Geometry to the rescue. In: European conference on computer vision. pp. 740–756. Springer (2016)
2016
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Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., Navab, N.: Deeper depth prediction with fully convolutional residual networks. In: 2016 Fourth international conference on 3D vision (3DV). pp. 239–248. IEEE (2016)
2016
Earlier work this paper cites.
Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., Brox, T.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4040–4048 (2016)
2016
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Son, K., Liu, M.Y., Taguchi, Y.: Learning to remove multipath distortions in time-of-flight range images for a robotic arm setup. In: 2016 IEEE International Conference on Robotics and Automation (ICRA). pp. 3390–3397. IEEE (2016)
2016
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Xie, J., Girshick, R., Farhadi, A.: Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks. In: European Conference on Computer Vision. pp. 842–857. Springer (2016)
2016
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Chang, A., Dai, A., Funkhouser, T., Halber, M., Niessner, M., Savva, M., Song, S., Zeng, A., Zhang, Y.: Matterport3d: Learning from rgb-d data in indoor environments. International Conference on 3D Vision (3DV) (2017)
2017
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Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: Scannet: Richly-annotated 3d reconstructions of indoor scenes. In: Proc. Computer Vision and Pattern Recognition (CVPR), IEEE (2017)
2017
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Godard, C., Aodha, O.M., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Jul 2017)
2017
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Kadambi, A., Taamazyan, V., Shi, B., Raskar, R.: Depth sensing using geometrically constrained polarization normals. International Journal of Computer Vision 125
2017
Cited alongside, same era.
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in PyTorch. In: NIPS-W (2017)
2017
Cited alongside, same era.
Yu, Y., Zhu, D., Smith, W.A.: Shape-from-polarisation: a nonlinear least squares approach. In: Proceedings of the IEEE International Conference on Computer Vision Workshops. pp. 2969–2976 (2017)
2017
Cited alongside, same era.
Fu, H., Gong, M., Wang, C., Batmanghelich, K., Tao, D.: Deep ordinal regression network for monocular depth estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2002–2011 (2018)
2018
Cited alongside, same era.
Ba, Y., Gilbert, A., Wang, F., Yang, J., Chen, R., Wang, Y., Yan, L., Shi, B., Kadambi, A.: Deep shape from polarization. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIV 16. pp. 554–571. Springer (2020)
2020
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Fang, H.S., Wang, C., Gou, M., Lu, C.: Graspnet-1billion: A large-scale benchmark for general object grasping. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11444–11453 (2020)
2020
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Huynh, L., Nguyen-Ha, P., Matas, J., Rahtu, E., Heikkilä, J.: Guiding monocular depth estimation using depth-attention volume. In: European Conference on Computer Vision. pp. 581–597. Springer (2020)
2020
Later among the works it cites.
Kalra, A., Taamazyan, V., Rao, S.K., Venkataraman, K., Raskar, R., Kadambi, A.: Deep polarization cues for transparent object segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8602–8611 (2020)
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Guo, Q., Frosio, I., Gallo, O., Zickler, T., Kautz, J.: Tackling 3d tof artifacts through learning and the flat dataset. In: The European Conference on Computer Vision (ECCV) (September 2018)
2018
Cited alongside, same era.
Mayer, N., Ilg, E., Fischer, P., Hazirbas, C., Cremers, D., Dosovitskiy, A., Brox, T.: What makes good synthetic training data for learning disparity and optical flow estimation? International Journal of Computer Vision 126
2018
Cited alongside, same era.
Smith, W.A., Ramamoorthi, R., Tozza, S.: Height-from-polarisation with unknown lighting or albedo. IEEE transactions on pattern analysis and machine intelligence 41
2018
Cited alongside, same era.
Yang, Z., Wang, P., Wang, Y., Xu, W., Nevatia, R.: Lego: Learning edge with geometry all at once by watching videos. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 225–234 (2018)
2018
Cited alongside, same era.
Yang, Z., Wang, P., Xu, W., Zhao, L., Nevatia, R.: Unsupervised learning of geometry from videos with edge-aware depth-normal consistency. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 32 (2018)
2018
Cited alongside, same era.
Yin, Z., Shi, J.: GeoNet: Unsupervised learning of dense depth, optical flow and camera pose. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (Jun 2018)
2018
Cited alongside, same era.
Agresti, G., Schaefer, H., Sartor, P., Zanuttigh, P.: Unsupervised domain adaptation for tof data denoising with adversarial learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
2019
Cited alongside, same era.
Busam, B., Hog, M., McDonagh, S., Slabaugh, G.: SteReFo: efficient image refocusing with stereo vision. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (2019)
2019
Cited alongside, same era.
2020
Later among the works it cites.
Lopez-Rodriguez, A., Busam, B., Mikolajczyk, K.: Project to adapt: Domain adaptation for depth completion from noisy and sparse sensor data. In: Proceedings of the Asian Conference on Computer Vision (2020)
2020
Later among the works it cites.
Luo, X., Huang, J.B., Szeliski, R., Matzen, K., Kopf, J.: Consistent video depth estimation. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH) 39
2020
Later among the works it cites.
Spencer, J., Bowden, R., Hadfield, S.: Defeat-net: General monocular depth via simultaneous unsupervised representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14402–14413 (2020)
2020
Later among the works it cites.
2021
Later among the works it cites.
Gasperini, S., Koch, P., Dallabetta, V., Navab, N., Busam, B., Tombari, F.: R4dyn: Exploring radar for self-supervised monocular depth estimation of dynamic scenes. In: 2021 International Conference on 3D Vision (3DV). pp. 751–760. IEEE (2021)
2021
Later among the works it cites.
Jung, H., Brasch, N., Leonardis, A., Navab, N., Busam, B.: Wild tofu: Improving range and quality of indirect time-of-flight depth with rgb fusion in challenging environments. In: 2021 International Conference on 3D Vision (3DV). pp. 239–248. IEEE (2021)
2021
Later among the works it cites.
Kopf, J., Rong, X., Huang, J.B.: Robust consistent video depth estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1611–1621 (2021)
2021
Later among the works it cites.
Lee, S., Lee, J., Kim, B., Yi, E., Kim, J.: Patch-wise attention network for monocular depth estimation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 1873–1881 (2021)
2021
Later among the works it cites.
Liu, X., Iwase, S., Kitani, K.M.: Stereobj-1m: Large-scale stereo image dataset for 6d object pose estimation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10870–10879 (2021)
2021
Later among the works it cites.
Miangoleh, S.M.H., Dille, S., Mai, L., Paris, S., Aksoy, Y.: Boosting monocular depth estimation models to high-resolution via content-adaptive multi-resolution merging. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9685–9694 (2021)
2021
Later among the works it cites.
Ruhkamp, P., Gao, D., Chen, H., Navab, N., Busam, B.: Attention meets geometry: Geometry guided spatial-temporal attention for consistent self-supervised monocular depth estimation. In: IEEE International Conference on 3D Vision (3DV) (December 2021)
2021
Later among the works it cites.
Wang, P., Manhardt, F., Minciullo, L., Garattoni, L., Meier, S., Navab, N., Busam, B.: Demograsp: Few-shot learning for robotic grasping with human demonstration. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 5733–5740. IEEE (2021)
2021
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Watson, J., Aodha, O.M., Prisacariu, V., Brostow, G., Firman, M.: The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth. In: Computer Vision and Pattern Recognition (CVPR) (2021)
2021
Later among the works it cites.
Yin, W., Zhang, J., Wang, O., Niklaus, S., Mai, L., Chen, S., Shen, C.: Learning to recover 3d scene shape from a single image. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn. (CVPR) (2021)
2021
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Verdie, Y., Song, J., Mas, B., Benjamin, B., Leonardis, A., , McDonagh, S.: Cromo: Cross-modal learning for monocular depth estimation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
Closest in time.
Wang, P., Jung, H., Li, Y., Shen, S., Srikanth, R.P., Garattoni, L., Meier, S., Navab, N., Busam, B.: Phocal: A multimodal dataset for category-level object pose estimation with photometrically challenging objects. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
Closest in time.