Planar Segmentation of RGBD Images Using Fast Linear Fitting and Markov Chain Monte Carlo
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Abstract
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.
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2012-05
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