HelisaDhamo

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

Helisa Dhamo

Contact

Helisa Dhamo

Helisa Dhamo

  • PhD candidate

  • Email: helisa.dhamo[at]tum.de

  • Address:

Chair for Computer Aided Medical Procedures & Augmented Reality
Fakultät für Informatik
Technische Universität München
Boltzmannstr. 3
85748 Garching b. München

News

  • February 2020: Two papers accepted at CVPR '20!
  • July 2019: One paper accepted at ICCV '19!
  • May 2019: One paper accepted for publication at PRL Journal!

Student Projects

Available
Master ThesisScene graph generation
(Helisa Dhamo, Federico Tombari)

Running
Master ThesisRethinking Deep Learning based Monocular Depth Prediction
(Helisa Dhamo, Federico Tombari)

Finished
Bachelor ThesisComparison of methods to produce a two-layered LDI representation from a single RGB image
(Helisa Dhamo, Federico Tombari)
Master ThesisSkin Lesion Segmentation on 3D Surfaces
(Helisa Dhamo, Federico Tombari)
Master ThesisDeep Learning for Semantic Segmentation of Human Bodies
(Helisa Dhamo, Federico Tombari, Prof. Nassir Navab)

Teaching

Education

May 2017 M.Sc. Computational Science and Engineering, TUM. Thesis topic: "Depth Prediction from Structured Light using Fully Convolutional Neural Networks"

Research Projects

My research interests include the application of deep learning in computer vision. In particular I work on ambiguous aspects of scene understanding, such as generating occluded regions and semantic manipulation.

Layered Depth Prediction

Given a single image, we regress a specific data representation called Layered Depth Image (LDI), which contains information about the occluded regions in the reference frame and can fill in occlusion gaps in case of small view changes.

In Peeking Behind Objects, we separate the given scene in two layers, a foreground and a background, on a pipeline based on depth prediction and object inpainting. We show the applicability of this representation in view synthesis.

In the object-driven approach, we incorporate scene understanding to decompose the scene such that it is represented as object layers as well as a layout (background) layer. This makes the number of layers that describe a scene dependent on the complexity of the scene. Additionally, besides view synthesis, it enables object removal from the original image. Visit Project Page

Scene Graphs

Scene graphs refer to a data representation that semantically describes a scene, composed of nodes (objects) as well as edges (relationships between objects). We propose using scene graphs for image manipulation as well as 3D scene understanding. Papers will be available soon.

Publications

2020
J. Wald, H. Dhamo, N. Navab, F. Tombari
Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions
IEEE Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, June 2020
The first two authors contributed equally.
(bib)
H. Dhamo, A. Farshad, I. Laina, N. Navab, G. D. Hager, F. Tombari, C. Rupprecht
Semantic Image Manipulation Using Scene Graphs
IEEE Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, June 2020
The first two authors contributed equally.
(bib)
2019
H. Dhamo, N. Navab, F. Tombari
Object-driven Multi-Layer Scene Decomposition from a Single Image
International Conference on Computer Vision (ICCV), Seoul, Korea, October 2019 (bib)
H. Dhamo, K. Tateno, I. Laina, N. Navab, F. Tombari
Peeking Behind Objects: Layered Depth Prediction from a Single Image
Pattern Recognition Letters, Vol. 125, 2019 (bib)

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Title: M.Sc.
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Firstname: Helisa
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Languages: English, German, Spanish
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Position: Scientific Staff
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Edit | Attach | Refresh | Diffs | More | Revision r1.36 - 24 Feb 2020 - 16:26 - HelisaDhamo

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