Scene particles: unregularized particle-based scene flow estimation.
Scene particles: unregularized particle-based scene flow estimation.
复制标题
场景粒子：基于非正则粒子的场景流估计。
DOI：
10.1109/tpami.2013.162
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发表时间：
2014
影响因子：
23.6
通讯作者：
Hadfield S
中科院分区：
文献类型：
--
作者：
Hadfield S
本文提出了一种用于估计场景流的算法，这是一种更丰富的光流 3D 模拟。该方法的运行速度比替代技术快几个数量级，并且非常适合通过并行实现进一步提高性能。该算法采用多种假设来处理运动模糊性，而不是传统的平滑度约束，消除了过度平滑误差，并比以前的技术水平对基准数据提供了显着的性能改进。该方法非常灵活，能够在任何设置中与外观和/或深度传感器的任意组合一起操作，如有必要，还可以同时估计结构和运动。此外，该算法随着时间的推移传播信息以解决歧义，而不是像当代方法那样在每个帧上执行孤立的估计。探索了在不牺牲多种假设优势的情况下平滑运动场的方法，并演示了遮挡估计的概率方法，分别使性能提高了 10% 和 15%。最后，描述了一种数据驱动的跟踪方法，并用于估计手语期间手部的 3D 轨迹，而无需对每个视点的复杂外观变化进行建模。
In this paper, an algorithm is presented for estimating scene flow, which is a richer, 3D analog 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 hypotheses 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 percent 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.