Contrastive Feature Bin Loss for Monocular Depth Estimation
Recently monocular depth estimation has achieved notable performance using encoder-decoder-based models. These models have utilized the Scale-Invariant Logarithmic (SILog) loss for effective training, leading to significant performance improvements. However, since the SILog loss is designed to reduc...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10926715/ |
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