Randomly connected neural networks for self-supervised monocular depth estimation

References

• Aleotti F, Tosi F, Poggi M, Mattoccia S. 2018. Generative adversarial networks for unsupervised monocular depth prediction. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops; September.
   Google Scholar
• Allan M, McLeod AJ, Wang CC, Rosenthal J, Hu Z, Gard N, Eisert P, Fu KX, Zeffiro T, Xia W, et al. 2021. Stereo correspondence and reconstruction of endoscopic data challenge. CoRR. abs/2101.01133. Available from: https://arxiv.org/abs/2101.01133
   Google Scholar
• Cao ZW Y, Shen C. 2018. Estimating depth from monocular images as classification using deep fully convolutional residual networks. p. 3174–3182. Available from: https://ieeexplore.ieee.org/document/8010878/authors#authors
   Google Scholar
• Clevert D, Unterthiner T, Hochreiter S. 2016. Fast and accurate deep network learning by exponential linear units (elus). In: Bengio Y, LeCun Y, editors. 4th International Conference on Learning Representations, ICLR 2016; 2016 May 2-4; San Juan, Puerto Rico: Conference Track Proceedings. Available from: http://arxiv.org/abs/1511.07289
   Google Scholar
• Eigen D, Puhrsch C, Fergus R. 2014. Depth map prediction from a single image using a multi-scale deep network. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, editors. Advances in neural information processing systems 27; Curran Associates, Inc, Palais des Congrès de Montréal, Montréal CANADA.; p. 2366–2374. Available from: http://papers.nips.cc/paper/5539-depth-map-prediction-from-a-single-image-using-a-multi-scale-deep-network.pdf.
   Google Scholar
• Fu H, Gong M, Wang C, Batmanghelich K, Tao D. 2018. Deep ordinal regression network for monocular depth estimation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2018(6):2002–2011.
   Google Scholar
• Garg R, Kumar BV, Carneiro G, Reid I. 2016. Unsupervised CNN for single view depth estimation: geometry to the rescue. In: European Conference on Computer Vision; Amsterdam, the Netherlands,: Springer; p. 740–756.
   Google Scholar
• Geiger A, Roser M, Urtasun R. 2010. Efficient large-scale stereo matching. ACCV. Berlin, Heidelberg: Springer
   Google Scholar
• Giannarou S, Visentini-Scarzanella M, Yang GZ. Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans Pattern Anal Mach Intell. 2013. Jan; 35(1):130–43. doi:https://doi.org/10.1109/TPAMI.2012.81. PMID: 22450819.
   Google Scholar
• Godard C, Mac Aodha O, Brostow GJ. 2017. Unsupervised monocular depth estimation with left-right consistency. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii.
   Google Scholar
• Godard, Clément, Oisin Mac Aodha and Gabriel J. Brostow. “Digging Into Self-Supervised Monocular Depth Estimation.” 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (2019): 3827–3837.
   Google Scholar
• He K, Zhang X, Ren S, Sun J 2015. Deep residual learning for image recognition. Computer Vision and Pattern Recognition (CVPR). abs/1512.03385. Available from: http://arxiv.org/abs/1512.03385
   Google Scholar
• Hu J, Shen L, Sun G. 2018. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah; June.
   Google Scholar
• Huang G, Liu Z, van der Maaten L, Weinberger KQ. 2017. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii.
   Google Scholar
• Jaderberg M, Simonyan K, Zisserman A, Kavukcuoglu K. 2015. Spatial transformer networks. In: Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R, editors. Advances in neural information processing systems 28; Curran Associates, Inc.; p. 2017–2025. Available from: http://papers.nips.cc/paper/5854-spatial-transformer-networks.pdf.
   Google Scholar
• Johnson J, Alahi A, Fei-Fei L 2016. Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision.
   Google Scholar
• Karnewar A, Wang O 2020. MSG-GAN: multi-scale gradients for generative adversarial networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020; 2020 Jun 13; Seattle, WA, USA: IEEE; p. 7796–7805. Available from: https://doi.org/10.1109/CVPR42600.2020.00782.
   Google Scholar
• Liang, Zhengfa, Yiliu Feng, Yulan Guo, Hengzhu Liu, Wei Chen, Linbo Qiao, Li Zhou and Jianfeng Zhang. “Learning for Disparity Estimation Through Feature Constancy.” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018): 2811–2820.
   Google Scholar
• Mao X, Li Q, Xie H, Lau RYK, Wang Z, Smolley SP. 2017. Least squares generative adversarial networks. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy; p. 2813–2821.
   Google Scholar
• Mayer N, Ilg E, Häusser P, Fischer P, Cremers D, Dosovitskiy A, Brox T. 2016. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016; 2016 Jun 27-30; Las Vegas, NV, USA: IEEE Computer Society; p. 4040–4048. Available from: https://doi.org/10.1109/CVPR.2016.438.
   Google Scholar
• Pilzer, Andrea, Dan Xu, Mihai Marian Puscas, Elisa Ricci and N. Sebe. 2018. “Unsupervised Adversarial Depth Estimation Using Cycled Generative Networks.” 2018 International Conference on 3D Vision (3DV) : 587–595.
   Google Scholar
• Poggi M, Tosi F, Mattoccia S 2018. Learning monocular depth estimation with unsupervised trinocular assumptions. 2018 International Conference on 3D Vision (3DV):324–333.
   Google Scholar
• Shen Z, Zhang M, Zhao H, Yi S, Li H. 2021. Efficient attention: attention with linear complexities. WACV.
   Google Scholar
• Shi W, Caballero J, Huszar F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z. 2016. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: CVPR; IEEE Computer Society,  Caesars Palace in Las Vegas, Nevada; p. 1874–1883. Available from: http://dblp.uni-trier.de/db/conf/cvpr/cvpr2016.html#ShiCHTABRW16
   Google Scholar
• Tanaka H, Kunin D, Yamins DL, Ganguli S 2020. Pruning neural networks without any data by iteratively conserving synaptic flow. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, editors. Advances in Neural Information Processing Systems; vol. 33. Curran Associates, Inc.; p. 6377–6389. Available from: https://proceedings.neurips.cc/paper/2020/file/46a4378f835dc8040c8057beb6a2da52-Paper.pdf
   Google Scholar
• Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I 2017. Attention is all you need. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, editors. Advances in Neural Information Processing Systems; vol. 30. Long Beach Convention Center, Long Beach: Curran Associates, Inc.; Available from: https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
   Google Scholar
• Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 13(4):600–612. Available from. doi:https://doi.org/10.1109/TIP.2003.819861.
   Google Scholar
• Watson J, Firman M, Brostow GJ, Turmukhambetov D 2019. Self-supervised monocular depth hints. In: The International Conference on Computer Vision (ICCV); October.
   Google Scholar
• Watts D, Strogatz S. 1998. Collective dynamics of ’small-world’ networks. Nature. 393(6684):440–442. doi:https://doi.org/10.1038/30918.
   Google Scholar
• Xie J, Girshick RB, Farhadi A 2016. Deep3d: fully automatic 2d-to-3d video conversion with deep convolutional neural networks. In: European Conf. Amsterdam, The Netherlands: Computer Vision (ECCV).
   Google Scholar
• Xie S, Kirillov A, Girshick RB, He K 2019. Exploring randomly wired neural networks for image recognition. 2019 IEEE/CVF International Conference on Computer Vision (ICCV); p. 1284–1293.
   Google Scholar
• Yamaguchi K, McAllester DA, Urtasun R 2014. Efficient joint segmentation, occlusion labeling, stereo and flow estimation. In: ECCV,  Zurich, Switzerland.
   Google Scholar
• Ye M, Johns E, Handa A, Zhang L, Pratt P, Yang G 2017. Self-supervised siamese learning on stereo image pairs for depth estimation in robotic surgery. ArXiv. abs/1705.08260.
   Google Scholar
• Yosinski J, Clune J, Bengio Y, Lipson H 2014. How transferable are features in deep neural networks? In: Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger KQ, editors. Advances in Neural Information Processing Systems; vol. 27. Curran Associates, Inc.; Available from: https://proceedings.neurips.cc/paper/2014/file/375c71349b295fbe2dcdca9206f20a06-Paper.pdf
   Google Scholar
• Zhou, Zongwei, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh and Jianming Liang. “UNet++: A Nested U-Net Architecture for Medical Image Segmentation.” Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, 11045 (2018): 3–11.
   Google Scholar