MiraSlavcheva

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

Mira Slavcheva

Mira

Contact

Miroslava Slavcheva

  • Email: mira.slavcheva[at]tum.de

  • Address:
Technische Universität München
Fakultät für Informatik
Boltzmannstr. 3
85748 Garching b. München, Deutschland
Siemens AG
Research & Technology Center
Otto-Hahn-Ring 6
81739 München, Deutschland

Research Interests

  • Rigid & Deformable 3D Reconstruction
  • Optimization
  • RGB-D Sensors

About

I am a PhD student at the Chair for Computer Aided Medical Procedures of TUM and Siemens AG under the supervision of PD Dr. Slobodan Ilic since March 2015. Before that I obtained an MSc degree in Computational Science and Engineering from TUM and a BSc in Computer Science from Jacobs University Bremen. My current research focuses on rigid and non-rigid 3D reconstruction with RGB-D sensors. In my free time I co-organize the Computer Vision and Medical Image Analysis Meetup with Fausto Milletari, and go rowing with the university team.

News

  • [03.03.2017] Our paper "KillingFusion: Non-rigid 3D Reconstruction without Correspondences" got accepted as a spotlight presentation at CVPR 2017.
  • [13.09.2016] We are going to demonstrate a 10 FPS version of the Siemens depth sensor used for the 3D-Printed RGB-D Object Dataset in SDF-2-SDF at ECCV 2016 (afternoon session on Thursday, 13. October).

Publications

2017
M. Slavcheva, M. Baust, D. Cremers, S. Ilic
KillingFusion: Non-rigid 3D Reconstruction without Correspondences
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, July 2017 [spotlight] (bib)
2016
M. Slavcheva, S. Ilic
SDF-TAR: Parallel Tracking and Refinement in RGB-D Data using Volumetric Registration
British Machine Vision Conference (BMVC), York, UK, September 2016 (abstract) (bib)
M. Slavcheva, W. Kehl, N. Navab, S. Ilic
SDF-2-SDF: Highly Accurate 3D Object Reconstruction
European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, October 2016 (supplementary) (bib)

Teaching Assistance

Dataset


The 3D-Printed Dataset introduced in the SDF-2-SDF paper is available here.
It contains groundtruth CAD models and camera trajectories for 5 objects, each of which is scanned both with turntable and handheld motion. We provide synthetic, industrial- and Kinect-quality RGB-D sequences.
Dataset

Projects

Implicit-to-Implicit Registration for Rigid-Body Motion Estimation

Kenny This is a method for precise registration from range data, in which pairs of signed distance fields are aligned by means of minimization of their direct per-voxel difference. Some of the advantages over cloud registration techniques are the absence of correspondence search and the denser formulation, which lead to higher accuracy and a larger convergence basin.
It is used as the basis for SDF-2-SDF, an object reconstruction pipeline that combines frame-to-frame camera tracking and multiview pose optimization, and SDF-TAR, an extension to larger scenes and SLAM.
The project started with my master thesis (Unified Pipeline for 3D Reconstruction from RGB-D Images using Coloured Truncated Signed Distance Fields), supervised by Wadim Kehl. It was selected for presentation at the Young Researcher Forum at GCPR 2015. Later, I got a chance to present the underlying approach as an oral at the WiCV workshop at CVPR 2016 (poster).

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