A search-classify approach for cluttered indoor scene understanding

Liangliang Nan*, Ke Xie, Andrei Sharf

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

155 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number137
JournalACM Transactions on Graphics
Volume31
Issue number6
DOIs
StatePublished - Nov 2012

Keywords

  • Point cloud classification
  • Reconstruction
  • Scene understanding

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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