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.
| Award: | (Best Student Paper Award) |
| Author(s): | Xue, Yuxuan and Li, Haolong and Leutenegger, Stefan and Stueckler, Joerg |
| Book Title: | Proceedings of the British Machine Vision Conference (BMVC) |
| Year: | 2022 |
| Project(s): | |
| Bibtex Type: | Conference Paper (inproceedings) |
| State: | Published |
| URL: | https://bmvc2022.mpi-inf.mpg.de/78/ |
| Award Paper: | Best Student Paper Award |
| Electronic Archiving: | grant_archive |
| Links: | |
BibTex
@inproceedings{xue2022evnonrigid,
title = {Event-based Non-Rigid Reconstruction from Contours},
aword_paper = {Best Student Paper Award},
booktitle = {Proceedings of the British Machine Vision Conference (BMVC)},
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.},
year = {2022},
slug = {xue2022evnonrigid},
author = {Xue, Yuxuan and Li, Haolong and Leutenegger, Stefan and Stueckler, Joerg},
url = {https://bmvc2022.mpi-inf.mpg.de/78/}
}