IEEE Trans Image Process - A weighted optimization approach to time-of-flight sensor fusion.

Tópicos

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Resumo

Acquiring scenery depth is a fundamental task in computer vision, with many applications in manufacturing, surveillance, or robotics relying on accurate scenery information. Time-of-flight cameras can provide depth information in real-time and overcome short-comings of traditional stereo analysis. However, they provide limited spatial resolution and sophisticated upscaling algorithms are sought after. In this paper, we present a sensor fusion approach to time-of-flight super resolution, based on the combination of depth and texture sources. Unlike other texture guided approaches, we interpret the depth upscaling process as a weighted energy optimization problem. Three different weights are introduced, employing different available sensor data. The individual weights address object boundaries in depth, depth sensor noise, and temporal consistency. Applied in consecutive order, they form three weighting strategies for time-of-flight super resolution. Objective evaluations show advantages in depth accuracy and for depth image based rendering compared with state-of-the-art depth upscaling. Subjective view synthesis evaluation shows a significant increase in viewer preference by a factor of four in stereoscopic viewing conditions. To the best of our knowledge, this is the first extensive subjective test performed on time-of-flight depth upscaling. Objective and subjective results proof the suitability of our approach to time-of-flight super resolution approach for depth scenery capture.

Resumo Limpo

acquir sceneri depth fundament task comput vision mani applic manufactur surveil robot reli accur sceneri inform timeofflight camera can provid depth inform realtim overcom shortcom tradit stereo analysi howev provid limit spatial resolut sophist upscal algorithm sought paper present sensor fusion approach timeofflight super resolut base combin depth textur sourc unlik textur guid approach interpret depth upscal process weight energi optim problem three differ weight introduc employ differ avail sensor data individu weight address object boundari depth depth sensor nois tempor consist appli consecut order form three weight strategi timeofflight super resolut object evalu show advantag depth accuraci depth imag base render compar stateoftheart depth upscal subject view synthesi evalu show signific increas viewer prefer factor four stereoscop view condit best knowledg first extens subject test perform timeofflight depth upscal object subject result proof suitabl approach timeofflight super resolut approach depth sceneri captur

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