Variational approaches to disparity estimation typically use a linearised
brightness constancy constraint, which only applies in smooth regions and over
small distances. Accordingly, current variational approaches rely on a schedule
to progressively include image data. This paper proposes the use of Gradient
Consistency information to assess the validity of the linearisation; this
information is used to determine the weights applied to the data term as part
of an analytically inspired Gradient Consistency Model. The Gradient
Consistency Model penalises the data term for view pairs that have a mismatch
between the spatial gradients in the source view and the spatial gradients in
the target view. Instead of relying on a tuned or learned schedule, the
Gradient Consistency Model is self-scheduling, since the weights evolve as the
algorithm progresses. We show that the Gradient Consistency Model outperforms
standard coarse-to-fine schemes and the recently proposed progressive inclusion
of views approach in both rate of convergence and accuracy.

这篇论文提出了使用梯度一致性信息来评估线性化的有效性，并将此信息用于确定作为解析梯度一致性模型的一部分应用于数据项的权重，以取代基于预定日程的数据库学术自调度，该模型通过分析得出的梯度一致性模型用于于目标视图的空间梯度和源视图的空间梯度之间不匹配的视图对的数据项。我们表明，梯度一致性模型在收敛速度和准确性方面优于标准的粗粒度到精细度方案和最近提出的逐步包含视图的方法。