Unsupervised segmentation of unknown objects in complex environments

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    10 Citations (Scopus)

    Abstract

    This paper presents a novel object segmentation approach for highly complex indoor scenes. Our approach starts with a novel algorithm which partitions the scene into distinct regions whose boundaries accurately conform to the physical object boundaries in the scene. Next, we propose a novel perceptual grouping algorithm based on local cues (e.g., 3D proximity, co-planarity, and shape convexity) to merge these regions into object hypotheses. Our extensive experimental evaluations demonstrate that our object segmentation results are superior compared to the state-of-the-art methods.
    Original languageEnglish
    Pages (from-to)805-829
    JournalAutonomous Robots
    Volume40
    Issue number5
    Early online date4 Sep 2015
    DOIs
    Publication statusPublished - Jun 2016

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