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Paper

To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons85396

Li,  Wenbin
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons197294

Azimi,  Seyedmajid
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44451

Fritz,  Mario
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1604.00066.pdf
(Preprint), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Li, W., Azimi, S., Leonardis, A., & Fritz, M. (2016). To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction. Retrieved from http://arxiv.org/abs/1604.00066.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002B-0634-6
Abstract
Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel object and their configurations. Developmental psychology has shown that such skills are acquired by infants from observations at a very early stage. In this paper, we contrast a more traditional approach of taking a model-based route with explicit 3D representations and physical simulation by an end-to-end approach that directly predicts stability and related quantities from appearance. We ask the question if and to what extent and quality such a skill can directly be acquired in a data-driven way bypassing the need for an explicit simulation. We present a learning-based approach based on simulated data that predicts stability of towers comprised of wooden blocks under different conditions and quantities related to the potential fall of the towers. The evaluation is carried out on synthetic data and compared to human judgments on the same stimuli.