A Search-Classify Approach for Cluttered Indoor Scene Understanding

Liangliang Nan SIAT, China Ke Xie SIAT, China Andrei Sharf Ben Gurion University, Israel

Geometric Modeling and Processing mathscidoc:1608.16109

SIGGRAPH/ACM Transactions on Graphics, 31, (6), 2012
We present an algorithm for recognition and reconstruction of scanned 3D indoor scenes. 3D indoor reconstruction is particularly challenging due to object interferences, occlusions and overlapping which yield incomplete yet very complex scene arrangements. Since it is hard to assemble scanned segments into complete models, traditional methods for object recognition and reconstruction would be inefficient. We present a search-classify approach which interleaves segmentation and classification in an iterative manner. Using a robust classifier we traverse the scene and gradually propagate classification information. We reinforce classification by a template fitting step which yields a scene reconstruction. We deform-to-fit templates to classified objects to resolve classification ambiguities. The resulting reconstruction is an approximation which captures the general scene arrangement. Our results demonstrate successful classification and reconstruction of cluttered indoor scenes, captured in just few minutes.
point cloud classiļ¬cation, scene understanding, reconstruction
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@inproceedings{liangliang2012a,
  title={A Search-Classify Approach for Cluttered Indoor Scene Understanding},
  author={Liangliang Nan, Ke Xie, and Andrei Sharf},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20160829140728769215536},
  booktitle={SIGGRAPH/ACM Transactions on Graphics},
  volume={31},
  number={6},
  year={2012},
}
Liangliang Nan, Ke Xie, and Andrei Sharf. A Search-Classify Approach for Cluttered Indoor Scene Understanding. 2012. Vol. 31. In SIGGRAPH/ACM Transactions on Graphics. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20160829140728769215536.
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