Geometric Proxies for Live RGB-D Stream Enhancement and Consolidation
This work addresses limitations in RGB-D sensors for indoor applications like modeling and robotics, offering a lightweight solution for real-time enhancement, though it is incremental as it builds on existing proxy-based methods.
The paper tackles the problem of low-quality RGB-D streams from sensors by proposing a geometric superstructure that uses compact local statistics over geometric proxies to enhance data in real-time, achieving noise removal, hole filling, and resampling with efficient embedded execution.
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.