Event-based Non-Rigid Reconstruction from Contours
Yuxuan Xue (Max Planck Institute for Intelligent Systems),* Haolong Li (Max Planck Institute for Intelligent Systems), Stefan Leutenegger (TU Munich), Joerg Stueckler (Max-Planck-Institute for Intelligent Systems)
The 33rd British Machine Vision Conference
Best Student Paper Award
Abstract
Visual reconstruction of fast non-rigid object deformations over time is a challenge for conventional frame-based cameras. In this paper, we propose a novel approach for reconstructing such deformations using measurements from event-based cameras. Our approach estimates the deformation of objects from events generated at the object contour in a probabilistic optimization framework. It associates events to mesh faces on the contour and maximizes the alignment of the line of sight through the event pixel with the associated face. In experiments on synthetic and real data, we demonstrate the advantages of our method over state-of-the-art optimization and learning-based approaches for reconstructing the motion of human hands.Video
Citation
@inproceedings{Xue_2022_BMVC,
author = {Yuxuan Xue and Haolong Li and Stefan Leutenegger and Joerg Stueckler},
title = {Event-based Non-Rigid Reconstruction from Contours},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0078.pdf}
}
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