- Email: mira.slavcheva[at]tum.de
- 3D Reconstruction
- Deformable Surface Tracking
- RGB-D Sensors
- [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).
||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)
||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)
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.
Implicit-to-Implicit Registration for Rigid-Body Motion Estimation
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).
| UsersForm |
| Title: || M.Sc. |
| Circumference of your head (in cm): || |
| Firstname: || Mira |
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| Lastname: || Slavcheva |
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| Groups: || Reconstruction, Computer Vision |
| Expertise: || Computer Vision |
| Position: || External Phd |
| Status: || Active |
| Emailbefore: || mira.slavcheva |
| Emailafter: || tum.de |
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