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.
@article{arxiv.2103.02451,
title = {Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation},
author = {Hemang Chawla and Arnav Varma and Elahe Arani and Bahram Zonooz},
journal= {arXiv preprint arXiv:2103.02451},
year = {2023}
}
Comments
Accepted at 2021 IEEE International Conference on Robotics and Automation (ICRA)