Unsupervised monocular depth estimation by optimizing with knowledge from Flow-Net with FG-Depth
Unsupervised monocular depth estimation by optimizing with knowledge from Flow-Net with FG-Depth
FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation
arXiv paper abstract https://arxiv.org/abs/2301.08414
arXiv PDF paper https://arxiv.org/pdf/2301.08414.pdf
The great potential of unsupervised monocular depth estimation has been demonstrated by many works due to low annotation cost and impressive accuracy comparable to supervised methods.
... However, previous methods prove that this image reconstruction optimization is prone to get trapped in local minima.
... core idea is to guide the optimization with prior knowledge from pretrained Flow-Net.
... show that the bottleneck of unsupervised monocular depth estimation can be broken with ... simple but effective framework named FG-Depth.
... propose (i) a flow distillation loss to replace the typical photometric loss that limits the capacity of the model and (ii) a prior flow based mask to remove invalid pixels that bring the noise in training loss.
... approach achieves state-of-the-art results on both KITTI and NYU-Depth-v2 datasets.
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