Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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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

  • [1]. Godard, Clément. et al. "Digging into self-supervised monocular depth estimation." ICCV2019.
  • [2]. Guizilini, Vitor. et al. "3D Packing for Self-Supervised Monocular Depth Estimation." CVPR2020
  • [3]. Ranftl, René. et al. "Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer." TPAMI2020.
  • [4]. Guizilini, Vitor. et al. "Semantically-Guided Representation Learning for Self-Supervised Monocular Depth. ICLR2020
  • [5]. Zhao, Wang, et al. "Towards Better Generalization: Joint Depth-Pose Learning without PoseNet?." CVPR2020
  • [6]. 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.

Yanyan Li

Federico Tombari

Title: Self-supervised Monocular Depth Estimation with Structure Regularities
Director: Federico Tombari
Supervisor: Yanyan Li
Type: Project
Status: draft
Thesis (optional):  

Edit | Attach | Refresh | Diffs | More | Revision r1.8 - 28 May 2021 - 11:51 - YanyanLi