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- W3135861835 abstract "Dense depth estimation is essential to scene-understanding for autonomous driving. However, recent self-supervised approaches on monocular videos suffer from scale-inconsistency across long sequences. Utilizing data from the ubiquitously copresent global positioning systems (GPS), we tackle this challenge by proposing a dynamically-weighted GPS-to-Scale (g2s) loss to complement the appearance-based losses. We emphasize that the GPS is needed only during the multimodal training, and not at inference. The relative distance between frames captured through the GPS provides a scale signal that is independent of the camera setup and scene distribution, resulting in richer learned feature representations. Through extensive evaluation on multiple datasets, we demonstrate scale-consistent and -aware depth estimation during inference, improving the performance even when training with low-frequency GPS data." @default.
- W3135861835 created "2021-03-15" @default.
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- W3135861835 date "2021-03-03" @default.
- W3135861835 modified "2023-09-26" @default.
- W3135861835 title "Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation" @default.
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