IEEE Trans Pattern Anal Mach Intell - Scene Particles: Unregularized Particle Based Scene Flow Estimation.

Tópicos

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{ model(3404) distribut(989) bayesian(671) }
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{ system(1976) rule(880) can(841) }
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{ imag(1057) registr(996) error(939) }
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Resumo

In this paper, an algorithm is presented for estimating scene flow, which is a richer, 3D analogue of Optical Flow. The approach operates orders of magnitude faster than alternative techniques, and is well suited to further performance gains through parallelized implementation. The algorithm employs multiple hypothesis to deal with motion ambiguities, rather than the traditional smoothness constraints, removing oversmoothing errors and providing significant performance improvements on benchmark data, over the previous state of the art. The approach is flexible, and capable of operating with any combination of appearance and/or depth sensors, in any setup, simultaneously estimating the structure and motion if necessary. Additionally, the algorithm propagates information over time to resolve ambiguities, rather than performing an isolated estimation at each frame, as in contemporary approaches. Approaches to smoothing the motion field without sacrificing the benefits of multiple hypotheses are explored, and a probabilistic approach to Occlusion estimation is demonstrated, leading to 10% and 15% improved performance respectively. Finally, a data driven tracking approach is described, and used to estimate the 3D trajectories of hands during sign language, without the need to model complex appearance variations at each viewpoint.

Resumo Limpo

paper algorithm present estim scene flow richer d analogu optic flow approach oper order magnitud faster altern techniqu well suit perform gain parallel implement algorithm employ multipl hypothesi deal motion ambigu rather tradit smooth constraint remov oversmooth error provid signific perform improv benchmark data previous state art approach flexibl capabl oper combin appear andor depth sensor setup simultan estim structur motion necessari addit algorithm propag inform time resolv ambigu rather perform isol estim frame contemporari approach approach smooth motion field without sacrif benefit multipl hypothes explor probabilist approach occlus estim demonstr lead improv perform respect final data driven track approach describ use estim d trajectori hand sign languag without need model complex appear variat viewpoint

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