KD-MVS: Knowledge Distillation Based Self-supervised Learning for Multi-view Stereo

Kazhdan, M., Hoppe, H.: Screened poisson surface reconstruction. ACM Transactions on Graphics (ToG) 32

2013

Earlier work this paper cites.

Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)

Original

2014

Earlier work this paper cites.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

Original

2014

Earlier work this paper cites.

Galliani, S., Lasinger, K., Schindler, K.: Massively parallel multiview stereopsis by surface normal diffusion. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 873–881 (2015)

2015

Earlier work this paper cites.

Hinton, G., Vinyals, O., Dean, J., et al.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 2

Original

2015

Earlier work this paper cites.

Aanæs, H., Jensen, R.R., Vogiatzis, G., Tola, E., Dahl, A.B.: Large-scale data for multiple-view stereopsis. International Journal of Computer Vision 120

2016

Earlier work this paper cites.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2016)

2016

Earlier work this paper cites.

Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European conference on computer vision. pp. 694–711. Springer (2016)

2016

Earlier work this paper cites.

Schönberger, J.L., Zheng, E., Frahm, J.M., Pollefeys, M.: Pixelwise view selection for unstructured multi-view stereo. In: European Conference on Computer Vision. pp. 501–518. Springer (2016)

2016

Earlier work this paper cites.

Zagoruyko, S., Komodakis, N.: Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928 (2016)

Original

2016

Earlier work this paper cites.

Huang, Z., Wang, N.: Like what you like: Knowledge distill via neuron selectivity transfer. arXiv preprint arXiv:1707.01219 (2017)

Original

2017

Earlier work this paper cites.

Knapitsch, A., Park, J., Zhou, Q.Y., Koltun, V.: Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics (ToG) 36

2017

Earlier work this paper cites.

Furlanello, T., Lipton, Z., Tschannen, M., Itti, L., Anandkumar, A.: Born again neural networks. In: International Conference on Machine Learning. pp. 1607–1616. PMLR (2018)

2018

Earlier work this paper cites.

Passalis, N., Tefas, A.: Learning deep representations with probabilistic knowledge transfer. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 268–284 (2018)

2018

Earlier work this paper cites.

Passalis, N., Tefas, A.: Learning deep representations with probabilistic knowledge transfer. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 268–284 (2018)

2018

Earlier work this paper cites.

Sun, D., Yang, X., Liu, M.Y., Kautz, J.: Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8934–8943 (2018)

2018

Earlier work this paper cites.

Yao, Y., Luo, Z., Li, S., Fang, T., Quan, L.: Mvsnet: Depth inference for unstructured multi-view stereo. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 767–783 (2018)

2018

Cited alongside, same era.

Dai, Y., Zhu, Z., Rao, Z., Li, B.: Mvs2: Deep unsupervised multi-view stereo with multi-view symmetry. In: 2019 International Conference on 3D Vision (3DV). pp. 1–8. IEEE (2019)

2019

Cited alongside, same era.

Khot, T., Agrawal, S., Tulsiani, S., Mertz, C., Lucey, S., Hebert, M.: Learning unsupervised multi-view stereopsis via robust photometric consistency. arXiv preprint arXiv:1905.02706 (2019)

Original

2019

Cited alongside, same era.

Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3967–3976 (2019)

2019

Cited alongside, same era.

Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves imagenet classification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10687–10698 (2020)

2020

Later among the works it cites.

Yan, J., Wei, Z., Yi, H., Ding, M., Zhang, R., Chen, Y., Wang, G., Tai, Y.W.: Dense hybrid recurrent multi-view stereo net with dynamic consistency checking. In: European Conference on Computer Vision. pp. 674–689. Springer (2020)

2020

Later among the works it cites.

Yang, J., Mao, W., Alvarez, J.M., Liu, M.: Cost volume pyramid based depth inference for multi-view stereo. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4877–4886 (2020)

2020

Later among the works it cites.

Yao, Y., Luo, Z., Li, S., Zhang, J., Ren, Y., Zhou, L., Fang, T., Quan, L.: Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1790–1799 (2020)