Semantically Coherent Co-Segmentation and Reconstruction of Dynamic Scenes

A. Mustafa, A. Hilton
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引用次数: 48

Abstract

In this paper we propose a framework for spatially and temporally coherent semantic co-segmentation and reconstruction of complex dynamic scenes from multiple static or moving cameras. Semantic co-segmentation exploits the coherence in semantic class labels both spatially, between views at a single time instant, and temporally, between widely spaced time instants of dynamic objects with similar shape and appearance. We demonstrate that semantic coherence results in improved segmentation and reconstruction for complex scenes. A joint formulation is proposed for semantically coherent object-based co-segmentation and reconstruction of scenes by enforcing consistent semantic labelling between views and over time. Semantic tracklets are introduced to enforce temporal coherence in semantic labelling and reconstruction between widely spaced instances of dynamic objects. Tracklets of dynamic objects enable unsupervised learning of appearance and shape priors that are exploited in joint segmentation and reconstruction. Evaluation on challenging indoor and outdoor sequences with hand-held moving cameras shows improved accuracy in segmentation, temporally coherent semantic labelling and 3D reconstruction of dynamic scenes.
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动态场景的语义连贯共分割与重构
在本文中,我们提出了一个框架,用于空间和时间上连贯的语义共分割和重建从多个静态或移动摄像机复杂的动态场景。语义共分割利用了语义类标签在空间上的一致性,即在单个时间瞬间的视图之间,以及在时间上,具有相似形状和外观的动态对象的大间隔时间瞬间之间。我们证明了语义连贯可以改善复杂场景的分割和重建。通过在视图和时间之间强制一致的语义标记,提出了一种基于语义连贯对象的场景共分割和重建的联合公式。引入语义轨迹是为了在广泛间隔的动态对象实例之间进行语义标记和重建时加强时间一致性。动态对象的轨迹可以实现外观和形状先验的无监督学习,用于联合分割和重建。用手持移动摄像机对具有挑战性的室内和室外序列进行评估,结果表明,在分割、时间连贯的语义标记和动态场景的3D重建方面,精度有所提高。
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