http://cs231n.stanford.edu/reports/2017/pdfs/200.pdf
1.multi-scale deep network, outperformed most other meth- ods in nearly every metric. Inspection of the output maps, however, shows that the images produced are extremely blurry. So while they are able to achieve low average er- ror, their utility for practical depth mapping applications is limited.
生成的深度图模糊，原因在于优化目标是平均像素误差。
2.CycleGAN is able to best retain the image features with clear definition, but often with high error in the depth-space representation.
生成深度图比较清晰，特征重建较好，而像素级误差较大，原因在于优化目标是特征级误差。
3.改进方向
设计损失函数，使其能同时优化像素级误差和特征级误差。
http://cs231n.stanford.edu/reports/2017/pdfs/200.pdf
1.multi-scale deep network, outperformed most other meth- ods in nearly every metric. Inspection of the output maps, however, shows that the images produced are extremely blurry. So while they are able to achieve low average er- ror, their utility for practical depth mapping applications is limited.
生成的深度图模糊，原因在于优化目标是平均像素误差。
2.CycleGAN is able to best retain the image features with clear definition, but often with high error in the depth-space representation.
生成深度图比较清晰，特征重建较好，而像素级误差较大，原因在于优化目标是特征级误差。
3.改进方向
设计损失函数，使其能同时优化像素级误差和特征级误差。