Self-supervised monocular depth estimation improves in low-texture regions by using distance transforms on jointly estimated pre-semantic contours to create more informative loss signals.
The temporal opportunist: Self-supervised multi-frame monocular depth
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CylinderDepth uses cylindrical spatial attention with non-learned weights to enforce cross-view consistency in self-supervised surround depth estimation.
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Improved monocular depth prediction using distance transform over pre-semantic contours with self-supervised neural networks
Self-supervised monocular depth estimation improves in low-texture regions by using distance transforms on jointly estimated pre-semantic contours to create more informative loss signals.
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CylinderDepth: Cylindrical Spatial Attention for Multi-View Consistent Self-Supervised Surround Depth Estimation
CylinderDepth uses cylindrical spatial attention with non-learned weights to enforce cross-view consistency in self-supervised surround depth estimation.