Monocular depth using transformer, CNN, and uncertainty with URCDC-Depth
Monocular depth using transformer, CNN, and uncertainty with URCDC-Depth
URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation
arXiv paper abstract https://arxiv.org/abs/2302.08149
arXiv PDF paper https://arxiv.org/pdf/2302.08149.pdf
This work aims to estimate a high-quality depth map from a single RGB image.
... introduce an uncertainty rectified cross-distillation between Transformer and convolutional neural network (CNN) to learn a unified depth estimator.
... use the depth estimates derived from the Transformer branch and the CNN branch as pseudo labels to teach each other.
... model the pixel-wise depth uncertainty to rectify the loss weights of noisy depth labels.
To avoid the large performance gap induced by the strong Transformer branch deteriorating the cross-distillation, ... transfer the feature maps from Transformer to CNN and design coupling units to assist the weak CNN branch to utilize the transferred features.
... URCDC-Depth, exceeds previous state-of-the-art methods ... with no additional computational burden at inference time ...
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