Name: Manuel Dahnert
Position: Ph.D Candidate
Phone: +49-89-289-18165
Room No: 02.07.034


I am a PhD student at the Visual Computing Lab with the focus on 3D shape analysis, generation and retrieval. I did my Master's Thesis in the same group about Transfer Learning between Synthetic and Real Data. This thesis concluded the Informatics: Games Engineering program (M.Sc) with specializations on Computer Graphics and Animation and Hardware-aware Programming. In 2015, I received my Bachelor's degree (B.Sc) Informatics: Games Engineering from the Technical University of Munich (TUM). From August 2016 until June 2017, I took part in the Erasmus mobility program, in which I was studying at Chalmers University of Technology, Sweden. In spring 2019, I spent three months at Stanford University visiting Geometric Computation group of Prof. Leonidas Guibas.

Research Interest

3D Deep Learning, Shape Matching, Generative Modeling, Domain Adaptation, Visualization



Joint Embedding of 3D Scan and CAD Objects
Manuel Dahnert, Angela Dai, Leonidas Guibas, Matthias Nießner
ICCV 2019
In this paper, we address the problem of cross-domain retrieval between partial, incomplete 3D scan objects and complete CAD models. To this end, we learn a joint embedding where semantically similar objects from both domains lie close together regardless of low-level differences, such as clutter or noise. To enable fine-grained evaluation of scan-CAD model retrieval we additionally present a new dataset of scan-CAD object similarity annotations.
[video][bibtex][project page]

Scan2CAD: Learning CAD Model Alignment in RGB-D Scans
Armen Avetisyan, Manuel Dahnert, Angela Dai, Angel X. Chang, Manolis Savva, Matthias Nießner
CVPR 2019 (Oral)
We present Scan2CAD, a novel data-driven method that learns to align clean 3D CAD models from a shape database to the noisy and incomplete geometry of a commodity RGB-D scan.
[video][code][bibtex][project page]