Furniture Recognition using Implicit Shape Models on 3D Data pdfsubject

The recognition and classification of objects in 3D scene data is still a very challenging task in computer vision. For robots acting in domestic environments especially the correct recognition of furniture objects is important because these belong to the main things they need to consider, either for navigating around or for using them. In this work an approach for the recognition of furniture objects in indoor room scenes is presented. The implemented method learns the spatial relationship of typical object regions by defining an Implicit Shape Model (ISM). For training the ISM, artificial 3D models are used, which are public available in several internet datasets. To recognize the appearances of the learned relationships in test scenes captured with a 3D sensor, a probabilistic Hough voting is performed, giving the ability to simultaneously recognize and localize instances of the learned object category. The implemented method is tested on four furniture categories: office chair, dining chair, dining table and couch.