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

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.

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

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arXiv:1604.00066.pdf (Preprint), 2MB
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 Creators:
Li, Wenbin1, Author           
Azimi, Seyedmajid1, Author           
Leonardis, Aleš2, Author
Fritz, Mario1, Author           
Affiliations:
1Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society, ou_1116547              
2External Organizations, ou_persistent22              

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Free keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Robotics, cs.RO
 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.

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Language(s): eng - English
 Dates: 2016-03-312016
 Publication Status: Published online
 Pages: 20 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1604.00066
URI: http://arxiv.org/abs/1604.00066
BibTex Citekey: Li_arXiv2016
 Degree: -

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