深度估计是计算机视觉领域的一个基础性问题,其可以应用在机器人导航、增强现实、三维重建、自动驾驶等领域。而目前大部分深度估计都是基于二维RGB图像到RBG-D图像的转化估计,主要包括从图像明暗、不同视角、光度、纹理信息等获取场景深度形状的Shape from X方法,还有结合SFM(Structure from motion)和SLAM(Simultaneous Localization And Mapping)等方式预测相机位姿的算法。其中虽然有很多设备可以直接获取深度,但是设备造价昂贵。也可以利用双目进行深度估计,但是由于双目图像需要利用立体匹配进行像素点对应和视差计算,所以计算复杂度也较高,尤其是对于低纹理场景的匹配效果不好。而单目深度估计则相对成本更低,更容易普及。
除此之外,在CVPR2018也有一篇类似的算法《Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer》,其效果则是达到了state-of-art,我们暂且称其为MDEDA,网络框架如下:
在ICRA2019中《Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations》中基于图像分割算法RefineNet设计了一个多任务框架。其中RefineNets是CVPR2017中提出的算法,其全局框架是基于Resnet的U-net网络框架,可以输出多尺度的分割图:
ECCV2018中《DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency》一文提出了单目深度估计和光流预测的联合任务框架。不同于单独训练两个任务的方式,作者将二者的一致性进行了考虑,从而做到二者的相互促进,可以看到对比效果:
我前段时间还发现一个多任务的集成框架CVPR2019的CCN算法《Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation》,效果目前好像还是SOTA,其融合了单目深度估计、相机位姿估计、光流估计和运动分割多个任务,代码:https://github.com/anuragranj/cc
[1] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 3431-3440.
[2] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]//International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015: 234-241.
[3] Laina I, Rupprecht C, Belagiannis V, et al. Deeper depth prediction with fully convolutional residual networks[C]//2016 Fourth international conference on 3D vision (3DV). IEEE, 2016: 239-248.
[4] Fu H, Gong M, Wang C, et al. Deep ordinal regression network for monocular depth estimation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 2002-2011.
[5] Godard C, Mac Aodha O, Brostow G J. Unsupervised monocular depth estimation with left-right consistency[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 270-279.
[6] Dosovitskiy A, Fischer P, Ilg E, et al. Flownet: Learning optical flow with convolutional networks[C]//Proceedings of the IEEE international conference on computer vision. 2015: 2758-2766.
[7] Ilg E, Mayer N, Saikia T, et al. Flownet 2.0: Evolution of optical flow estimation with deep networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 2462-2470.
[8] Mayer N, Ilg E, Hausser P, et al. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 4040-4048.
[9] Xie J, Girshick R, Farhadi A. Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks[C]//European Conference on Computer Vision. Springer, Cham, 2016: 842-857.
[10] Luo Y, Ren J, Lin M, et al. Single View Stereo Matching[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
[11] Zhou T, Brown M, Snavely N, et al. Unsupervised learning of depth and ego-motion from video[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 1851-1858.
[12] Yin Z, Shi J. Geonet: Unsupervised learning of dense depth, optical flow and camera pose[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 1983-1992.
[13] Zhan H, Garg R, Saroj Weerasekera C, et al. Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 340-349.
[14] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in neural information processing systems. 2014: 2672-2680.
[15] Radford A , Metz L , Chintala S . Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[J]. Computer Science, 2015.
[16] Arjovsky M, Chintala S, Bottou L. Wasserstein gan[J]. arXiv preprint arXiv:1701.07875, 2017.
[17] Gulrajani I, Ahmed F, Arjovsky M, et al. Improved training of wasserstein gans[C]//Advances in Neural Information Processing Systems. 2017: 5767-5777.
[18] Mao X, Li Q, Xie H, et al. Least squares generative adversarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 2794-2802.
[19] Mirza M, Osindero S. Conditional generative adversarial nets[J]. arXiv preprint arXiv:1411.1784, 2014.
[20] Isola P, Zhu J Y, Zhou T, et al. Image-to-image translation with conditional adversarial networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1125-1134.
[21] Wang T C, Liu M Y, Zhu J Y, et al. High-resolution image synthesis and semantic manipulation with conditional gans[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 8798-8807.
[22] Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2223-2232.
[23] Wang T C , Liu M Y , Zhu J Y , et al. Video-to-Video Synthesis[J]. arXiv preprint arXiv:1808.06601,2018.
[24] Zheng C, Cham T J, Cai J. T2net: Synthetic-to-realistic translation for solving single-image depth estimation tasks[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 767-783.
[25] Atapour-Abarghouei A, Breckon T P. Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 2800-2810.
[26] Nekrasov V , Dharmasiri T , Spek A , et al. Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations[J]. arXiv preprint arXiv:1809.04766,2018.
[27] Nekrasov V , Shen C , Reid I . Light-Weight RefineNet for Real-Time Semantic Segmentation[J]. arXiv preprint arXiv:1810.03272, 2018.
[28] Lin G , Milan A , Shen C , et al. RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.,2017:1925-1934
[29] Zou Y , Luo Z , Huang J B . DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018:36-53.
[30] Ranjan A, Jampani V, Balles L, et al. Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 12240-12249.