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Planar Segmentation of RGBD Images Using Fast Linear Fitting and Markov Chain Monte Carlo

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3 Author(s)
Erdogan, C. ; Sch. of Interactive Comput., Georgia Inst. of Technol., Atlanta, GA, USA ; Paluri, M. ; Dellaert, F.

With the advent of affordable RGBD sensors such as the Kinect, the collection of depth and appearance information from a scene has become effortless. However, neither the correct noise model for these sensors, nor a principled methodology for extracting planar segmentations has been developed yet. In this work, we advance the state of art with the following contributions: we correctly model the Kinect sensor data by observing that the data has inherent noise only over the measured disparity values, we formulate plane fitting as a linear least-squares problem that allow us to quickly merge different segments, and we apply an advanced Markov Chain Monte Carlo (MCMC) method, generalized Swendsen-Wang sampling, to efficiently search the space of planar segmentations. We evaluate our plane fitting and surface reconstruction algorithms with simulated and real-world data.

Published in:

Computer and Robot Vision (CRV), 2012 Ninth Conference on

Date of Conference:

28-30 May 2012