MaInstDepth

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

Rethinking Deep Learning based Monocular Depth Prediction

We are looking for a motivated student who wants to work in the topic of monocular depth prediction using deep learning. Predicting depth maps from a single color image is a challenging and under-constrained task, and as such, active research is happening that incorporates CNNs. Recent works typically do not explicitly enforce an understanding for the objects in the scene. In contrast, our goal is to rethink depth prediction and create an object-aware model, which might lead to more accurate depth.

Prerequisites: Good python skills and basic deep learning experience.

Supervision: Helisa Dhamo, PD Dr. Federico Tombari

For further information please contact: Helisa Dhamo

ProjectForm
Title: Rethinking Deep Learning based Monocular Depth Prediction
Abstract: We are looking for a motivated student who wants to work in the topic of monocular depth prediction using deep learning. Predicting depth from a single color image is a challenging and under-constrained task, and as such, active research is happening that incorporates CNNs. Recent works typically do not enforce an understanding for the objects in the scene. In contrast, our goal is to rethink depth prediction and create an object-aware model, which might lead to more accurate depth.
Student:  
Director: Federico Tombari
Supervisor: Helisa Dhamo
Type: Master Thesis
Area: Machine Learning, Computer Vision
Status: running
Start:  
Finish:  
Thesis (optional):  
Picture:  


Edit | Attach | Refresh | Diffs | More | Revision r1.3 - 15 Jun 2019 - 13:00 - HelisaDhamo