Official Repository for
Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimation
Daoyi Gao, Hanzhi Chen, Patrick Ruhkamp, Nassir Navab, Benjamin Busam - 3DV, 2021.
Daoyi Gao, Hanzhi Chen, Patrick Ruhkamp, Nassir Navab, Benjamin Busam - ICCV Workshop on Self-supervised Learning for Next-Generation Industry-level Autonomous Driving, 2021.
Bold: equal contribution
- Current SOTA in self-supervised monocular depth estimation achievies highly accurate depth predictions, but suffer from inconsistencies across temporal frames
- Our novel Spatial-Temporal Attention mechanism with Geometric Guidance improves consistency while maintaining accuracy
- The Temporal Consistency Metric (TCM) is a quantitative measure to evaluate the consistency between temporal predictions in 3D
- Pretrained weight available (04.11.2022)
- Release training code (02.10.2022)
- Evaluation code for TCM available (02.12.2021)
3 Frames Track | 5 Frames Track | 7 Frames Track
If you find our work useful, please consider citing the following papers:
@inproceedings{ruhkamp2021attention,
title = {Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimation},
author = {Patrick Ruhkamp and Daoyi Geo and Hanzhi Chen and Nassir Navab and Benjamin Busam},
booktitle = {IEEE International Conference on 3D Vision (3DV)},
year = {2021},
month = {December}
}
@article{monodepth2,
title = {Digging into Self-Supervised Monocular Depth Prediction},
author = {Cl{\'{e}}ment Godard and Oisin {Mac Aodha} and Michael Firman and Gabriel J. Brostow},
booktitle = {The International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}Our implementation is based on MonoDepth2 and follows their code structure. Thanks for their great contribution :)