Scene Flow Estimation using Intelligent Cost Functions
Scene Flow Estimation using Intelligent Cost Functions
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DOI：
10.5244/c.28.108
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发表时间：
2014-09
期刊：
影响因子：
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通讯作者：
Simon Hadfield;R. Bowden
中科院分区：
文献类型：
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作者：
Simon Hadfield;R. Bowden
运动估计算法通常基于亮度恒定性的假设或诸如梯度恒定性的相关假设。本手稿评估了运动估计文献中的几个常见成本函数，这些函数体现了这些假设。我们证明这种假设对于现实世界的数据来说是不成立的，因此这些函数是不合适的。我们提出了一个简单的解决方案，通过使用机器学习技术学习非线性关系，显着提高了度量的判别能力。此外，我们还展示了上下文和指标的非线性组合如何提供额外的收益，并展示了最先进的场景流估计技术的性能提高了 44%。此外，在光流估计任务中还展示了 20% 的较小增益。
Motion estimation algorithms are typically based upon the assumption of brightness constancy or related assumptions such as gradient constancy. This manuscript evaluates several common cost functions from the motion estimation literature, which embody these assumptions. We demonstrate that such assumptions break for real world data, and the functions are therefore unsuitable. We propose a simple solution, which significantly increases the discriminatory ability of the metric, by learning a nonlinear relationship using techniques from machine learning. Furthermore, we demonstrate how context and a nonlinear combination of metrics, can provide additional gains, and demonstrating a 44% improvement in the performance of a state of the art scene flow estimation technique. In addition, smaller gains of 20% are demonstrated in optical flow estimation tasks.