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Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments
Affordance modeling plays an important role in visual understanding. In ...
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Efficient Plane-Based Optimization of Geometry and Texture for Indoor RGB-D Reconstruction
We propose a novel approach to reconstruct RGB-D indoor scene based on p...
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Plane-Based Optimization of Geometry and Texture for RGB-D Reconstruction of Indoor Scenes
We present a novel approach to reconstruct RGB-D indoor scene with plane...
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RGB-D Odometry and SLAM
The emergence of modern RGB-D sensors had a significant impact in many a...
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Real-time 3D scene description using Spheres, Cones and Cylinders
The paper describes a novel real-time algorithm for finding 3D geometric...
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Depth Ranging Performance Evaluation and Improvement for RGB-D Cameras on Field-Based High-Throughput Phenotyping Robots
RGB-D cameras have been successfully used for indoor High-ThroughpuT Phe...
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Purely Geometric Scene Association and Retrieval - A Case for Macro Scale 3D Geometry
We address the problems of measuring geometric similarity between 3D sce...
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Geometric Proxies for Live RGB-D Stream Enhancement and Consolidation
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.
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