Semantic Segmentation of Modular Furniture

Tobias Pohlen, Ishrat Badami, Markus Mathias, Bastian Leibe
IEEE Winter Conference on Applications of Computer Vision (WACV'16)

This paper proposes an approach for the semantic seg- mentation and structural parsing of modular furniture items, such as cabinets, wardrobes, and bookshelves, into so called interaction elements. Such a segmentation into functional units is challenging not only due to the visual similarity of the different elements but also because of their often uniformly colored and low-texture appearance. Our method addresses these challenges by merging structural and appearance likelihoods of each element and jointly op- timizing over shape, relative location, and class labels us- ing Markov Chain Monte Carlo (MCMC) sampling. We propose a novel concept called rectangle coverings which provides a tight bound on the number of structural elements and hence narrows down the search space. We evaluate our approach’s performance on a novel dataset of furniture items and demonstrate its applicability in practice.

» Show BibTeX
@inproceedings{badamiWACV17, title={Semantic Segmentation of Modular Furniture}, author={Pohlen, Tobias and Badami, Ishrat and Mathias, Markus and Leibe, Bastian}, booktitle={WACV}, year={2016} }



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