Anytime Perceptual Grouping of 2D Features into 3D Basic Shapes Andreas Richtsfeld, Michael Zillich, and Markus Vincze Automation and Control Institute (ACIN), Vienna University of Technology Gusshausstrae 25-29, 1040 Vienna, Austria {ari,mz,mv}@acin.tuwien.ac.at Abstract. 2D perceptual grouping is a well studied area which still has its merits even in the age of powerful object recognizer, namely when no prior object knowledge is available. Often perceptual grouping mechanisms struggle with the runtime complexity stemming from the combinatorial explosion when creating larger assemblies of features, and simple thresholding for pruning hypotheses leads to cumbersome tuning of parameters. In this work we propose an incremental approach instead, which leads to an anytime method, where the system produces more results with longer runtime. Moreover the proposed approach lends itself easily to incorporation of attentional mechanisms. We show how basic 3D object shapes can thus be detected using a table plane assumption. Keywords: Computer vision, perceptual organization, object detection, basic shapes, proto-objects. 1 Introduction and Related Work Recognition methods based on powerful feature descriptors have lead to impres- sive results in object instance recognition and object categorization. In some scenarios, however, no prior object knowledge can be assumed and more generic object segmentation methods are required. This is the realm of perceptual group- ing, the application of generic principles for grouping certain image features into assemblies that are likely to correspond to objects in the scene. These principles are of course well studied, starting with the pioneering work in Gestalt psychology by Wertheimer, K¨ ohler, Koffka and Metzger. Gestalt prin- ciples (or Gestalt laws ) aim to formulate the regularities according to which the perceptual input is organized into unitary forms, also referred to as wholes, groups or Gestalts. Typical Gestalt principles are proximity, continuity, similar- ity and closure, as well as common fate, past experience and good Gestalt (form) ([22,23,7,6,9]). Common region and element connectedness were later introduced and discussed by Rock and Palmer [12,10,11]. The perceptual grouping literature has largely focused on grouping of edges, especially detecting the enclosing contours of objects. While learning algorithms have been mainly used for object recognition, Sarkar and Soundararajan [16,18] M. Chen, B. Leibe, and B. Neumann (Eds.): ICVS 2013, LNCS 7963, pp. 73–82, 2013. c  Springer-Verlag Berlin Heidelberg 2013