Self-supervised Monocular Depth Estimation with Structure Regularities
We offer a master (or guided research) topic in the field of autonomous driving, which aims at providing accurate dense depth maps in a self-supervised manner.
Self-supervised networks have achieved impressive performance in monocular depth prediction, but those depth maps cannot capture some small but important information like road-bump. To address this issue, we will propose a new framework generating more useful information rather than a rough depth map for the autonomous driving task.
Resources and Literature
- . Godard, Clément. et al. "Digging into self-supervised monocular depth estimation." ICCV2019.
- . Guizilini, Vitor. et al. "3D Packing for Self-Supervised Monocular Depth Estimation." CVPR2020
- . Ranftl, René. et al. "Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer." TPAMI2020.
- . Guizilini, Vitor. et al. "Semantically-Guided Representation Learning for Self-Supervised Monocular Depth. ICLR2020
- . Zhao, Wang, et al. "Towards Better Generalization: Joint Depth-Pose Learning without PoseNet?." CVPR2020
- . Irani, Michal, et al. "Parallax geometry of pairs of points for 3d scene analysis." ECCV1996.
- A basic background in Multi-View Geometry (SLAM/SfM).
- Familiar with self-supervised depth prediction projects.
- Experience in training networks.
If you are interested in this topic, please contact us via email.
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| Title: || Self-supervised Monocular Depth Estimation with Structure Regularities |
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| Director: || Federico Tombari |
| Supervisor: || Yanyan Li |
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| Status: || draft |
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