This paper presents an algorithm for surfel color and position enhancement from RGB-D data acquired across multiple image frames. Surfel-based reconstruction algorithms associate each RGB-D frame pixel to a surfel in the model. As the reconstruction progresses, surfel color and position are the average of all observations. Our proposed algorithm is designed to enhance position discontinuities and to produce sharper colors, to facilitate subsequent segmentation steps on the 3D model. During reconstruction, several colors and positions are tracked for each surfel. Only at the end of reconstruction phase the most frequent value is chosen through a Winner Takes All policy. The result has been compared to the standard averaging policy of reconstruction algorithms. Experiments have been performed using both Flood Fill and Supervoxel-LCCP segmentation and by applying two segmentation evaluation metrics. Results show that the proposed method is suitable to enhance a surfel-based model for object segmentation purposes.

RGB-D fusion enhancement by mode filter for surfel cloud segmentation / Monica, Riccardo; Zillich, Michael; Vincze, Markus; Aleotti, Jacopo. - 2017-(2017), pp. 6490-6497. ((Intervento presentato al convegno 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 tenutosi a can nel 2017 [10.1109/IROS.2017.8206557].

RGB-D fusion enhancement by mode filter for surfel cloud segmentation

Monica, Riccardo;Aleotti, Jacopo
2017

Abstract

This paper presents an algorithm for surfel color and position enhancement from RGB-D data acquired across multiple image frames. Surfel-based reconstruction algorithms associate each RGB-D frame pixel to a surfel in the model. As the reconstruction progresses, surfel color and position are the average of all observations. Our proposed algorithm is designed to enhance position discontinuities and to produce sharper colors, to facilitate subsequent segmentation steps on the 3D model. During reconstruction, several colors and positions are tracked for each surfel. Only at the end of reconstruction phase the most frequent value is chosen through a Winner Takes All policy. The result has been compared to the standard averaging policy of reconstruction algorithms. Experiments have been performed using both Flood Fill and Supervoxel-LCCP segmentation and by applying two segmentation evaluation metrics. Results show that the proposed method is suitable to enhance a surfel-based model for object segmentation purposes.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2838793
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