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Geometric Proxies for Live RGB-D Stream Enhancement and Consolidation | | Adrien Kaiser
; José Alonso Ybanez Zepeda
; Tamy Boubekeur
; | | Date: |
21 Jan 2020 | | Abstract: | We propose a geometric 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
consists in generating and updating through time a single set of compact local
statistics parameterized over detected geometric proxies, which are fed from
raw RGB-D data. Our proxies 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 lightweight analysis
framework copes well with embedded execution as well as moderate memory and
computational capabilities compared to state-of-the-art methods. Processing
RGB-D data with our proxies allows noise and temporal flickering removal, hole
filling and resampling. As a substitute of the observed scene, our proxies 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. | | Source: | arXiv, 2001.7577 | | Services: | Forum | Review | PDF | Favorites | |
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