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Robust Plane Detection Using Depth Information From a Consumer Depth Camera

Zhi Jin,T. Tillo,Wenbin Zou,Yao Zhao,Xia Li

Published 2019 in IEEE transactions on circuits and systems for video technology (Print)

ABSTRACT

The emerging of depth-camera technology is paving the way for a variety of new applications and it is believed that plane detection is one of them. In fact, planes are common in man-made living structures, thus their accurate detection can benefit many visual-based applications. The use of depth information allows detecting planes characterized by complex pattern and texture, where the texture-based plane detection algorithms usually fail. In this paper, we propose a robust depth-driven plane detection (DPD) algorithm which consists of two parts: the growing-based plane detection and a two-stage refinement. The proposed approach starts from the seed patch with the highest planarity and uses the estimated equation of the growing plane and a dynamic threshold function to steer the growing process. Aided with this mechanism, each seed patch can grow to its maximum extent, and then the next seed patch starts to grow. This process is iteratively repeated so as to detect all the planes. Moreover, the refinement is proposed to tackle two common problems suffered by growing-based approaches, the over-growing problem, and the under-growing problem. Validated by extensive experiments, the proposed DPD algorithm is able to accurately detect planes and robust to various testing conditions. In terms of applications, it can be used as the pre-processing step for a variety of applications, such as, planar object recognition, super-resolution of the time-of-flight depth images with intrinsically low resolution.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    IEEE transactions on circuits and systems for video technology (Print)

  • Publication date

    2019-02-01

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1109/TCSVT.2017.2780181
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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