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.

翻译：变分法视差估计通常采用线性化的亮度恒常约束，该约束仅适用于平滑区域和小位移场景。因此，当前变分方法依赖渐进式图像数据引入策略。本文提出利用梯度一致性信息评估线性化有效性，该信息通过解析启发的梯度一致性模型确定数据项权重。梯度一致性模型对源视图与目标视图间空间梯度失配的视角对施加数据项惩罚。该模型无需依赖调参或学习策略，其权重随算法进程自适应调整，实现自主调度。实验表明，梯度一致性模型在收敛速度和精度上均优于传统由粗到细方案及近期提出的渐进视角引入方法。