Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation

• URL: http://arxiv.org/abs/2103.02451v1
• Date: Wed, 3 Mar 2021 15:39:41 GMT
• Title: Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation
• Authors: Hemang Chawla, Arnav Varma, Elahe Arani, Bahram Zonooz
• Abstract summary: Self-supervised approaches on monocular videos suffer from scale-inconsistency across long sequences. We propose a dynamically-weighted GPS-to-Scale (g2s) loss to complement the appearance-based losses. We demonstrate scale-consistent and -aware depth estimation during inference, improving the performance even when training with low-frequency GPS data.
• Score: 1.1470070927586016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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