Abstract. We propose a new multiplanar superstructure for unified
real-time processing of RGB-D data. Modern RGB-D sensors are widely
used for indoor 3D capture, with applications ranging from modeling to
robotics, through augmented reality. Nevertheless, their use is limited
by their low resolution, with frames often corrupted with noise, missing
data and temporal inconsistencies. Our approach, named Proxy Clouds,
consists in generating and updating through time a single set of compact local statistics parameterized over detected planar proxies, which
are fed from raw RGB-D data. Proxy Clouds provide several processing
primitives, which improve the quality of the RGB-D stream on-the-fly or
lighten further operations. Experimental results confirm that our light
weight analysis framework copes well with embedded execution as well
as moderate memory and computational capabilities compared to stateof-the-art methods. Processing of RGB-D data with Proxy Clouds includes noise and temporal flickering removal, hole filling and resampling.
As a substitute of the observed scene, our proxy cloud can additionally
be applied to compression and scene reconstruction. We present experiments performed with our framework in indoor scenes of different natures
within a recent open RGB-D dataset