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Convolutional neural networks (CNNs) have become increasingly popular for solving a variety of computer vision tasks, ranging from image classification to image segmentation.
An iterative image registration technique with an application to stereo vision
Lucas, B. D. and Kanade, T. (1981) · 1981
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Object recognition from local scale-invariant features
Lowe, D. G. (1999) · 1999
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Vision-based mobile robot localization and mapping using scale-invariant features
Se, S., Lowe, D., and Little, J. (2001) · 2001
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A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Scharstein, D. and Szeliski, R. (2002) · 2002
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Learning depth from stereo
Sinz, F. H., Candela, J. Q., Bakır, G. H., Rasmussen, C. E., and Franz, M. O. (2004) · 2004
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Real-time stereo visual odometry for autonomous ground vehicles
Howard, A. (2008) · 2008
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Make3D: Learning 3d scene structure from a single still image
Saxena, A., Sun, M., and Ng, A. Y. (2009) · 2009
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Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., and Urtasun, R. (2012) · 2012
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Continuous markov random fields for robust stereo estimation
Yamaguchi, K., Hazan, T., McAllester, D., and Urtasun, R. (2012) · 2012
Cited alongside, same era.
Depth map prediction from a single image using a multi-scale deep network
Eigen, D., Puhrsch, C., and Fergus, R. (2014) · 2014
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Stereopsis via deep learning
Memisevic, R. and Conrad, C. (2014) · 2014
Cited alongside, same era.
ShapeNet: An information-rich 3d model repository
Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., and Yu, F. (2015) · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., and Darrell, T. (2015) · 2015
Cited alongside, same era.
Enet: A deep neural network architecture for real-time semantic segmentation
Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016) · 2016
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Speeding up semantic segmentation for autonomous driving
Treml, M., Arjona-Medina, J., Unterthiner, T., Durgesh, R., Friedmann, F., Schuberth, P., Mayr, A., Heusel, M., Hofmarcher, M., Widrich, M., Bodenhofer, U., Nessler, B., and Hochreiter, S. (2016) · 2016
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Wu, B., Iandola, F., Jin, P. H., and Keutzer, K. (2016) · 2016
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017) · 2017
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Redmon, J., Divvala, S. K., Girshick, R. B., and Farhadi, A. (2015) · 2015
Cited alongside, same era.
SqueezeNet: Alexnet-level accuracy with 50x fewer parameters and < < 0.5mb model size
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., and Keutzer, K. (2016) · 2016
Cited alongside, same era.
V-net: Fully convolutional neural networks for volumetric medical image segmentation
Milletari, F., Navab, N., and Ahmadi, S. (2016) · 2016
Cited alongside, same era.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2017) · 2017
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Voxelnet: End-to-end learning for point cloud based 3d object detection
Zhou, Y. and Tuzel, O. (2017) · 2017
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Deep Ordinal Regression Network for Monocular Depth Estimation
Fu, H., Gong, M., Wang, C., Batmanghelich, K., and Tao, D. (2018) · 2018
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