DcSConv adapts convolution filter scales based on object depth to reduce size-depth ambiguity in self-supervised monocular depth estimation, improving performance on KITTI by up to 11.6% in SqRel.
Predicting depth, surface normals and semantic labels with a common multi- scale convolutional architecture,
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Adaptive Depth-converted-Scale Convolution for Self-supervised Monocular Depth Estimation
DcSConv adapts convolution filter scales based on object depth to reduce size-depth ambiguity in self-supervised monocular depth estimation, improving performance on KITTI by up to 11.6% in SqRel.