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Real-Time Large-Scale Dense 3D Reconstruction with Loop Closure

机译:带环闭合的实时大规模密集3D重建

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In the highly active research field of dense 3D reconstruction and modelling, loop closure is still a largely unsolved problem. While a number of previous works show how to accumulate keyframes, globally optimize their pose on closure, and compute a dense 3D model as a post-processing step, in this paper we propose an online framework which delivers a consistent 3D model to the user in real time. This is achieved by splitting the scene into submaps, and adjusting the poses of the submaps as and when required. We present a novel technique for accumulating relative pose constraints between the submaps at very little computational cost, and demonstrate how to maintain a lightweight, scalable global optimization of submap poses. In contrast to previous works, the number of submaps grows with the observed 3D scene surface, rather than with time. In addition to loop closure, the paper incorporates relocalization and provides a novel way of assessing tracking quality.
机译:在密集3D重建和建模的高度活跃研究领域中,循环闭合仍然是一个很大程度上未解决的问题。虽然一些以前的作品展示了如何累积关键帧,但全局优化它们的封闭姿势,并将密集的3D模型作为后处理步骤进行计算,在本文中,我们提出了一个在线框架,该框架向用户提供一致的3D模型,为用户提供一致的3D模型即时的。这是通过将场景分成模板来实现,并在需要时调整子片的姿势。我们介绍了一种新的技术,用于在非常少的计算成本上累积模板之间的相对姿势约束,并演示如何维持模块姿势的轻量级,可扩展的全局优化。与以前的作品相比,子片的数量随观察到的3D场景表面而不是时间增长。除了环形封闭外,本文还包含重复定量,并提供评估跟踪质量的新方法。

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