论文标题
ACED：准确和边缘一致的单眼估计
AcED: Accurate and Edge-consistent Monocular Depth Estimation
论文作者
论文摘要
单图像深度估计是一个具有挑战性的问题。当前的最新方法将问题提出为序数回归的问题。但是，该配方并不是完全可区分的，并且深度图不会以端到端的方式生成。该方法使用幼稚的阈值策略来确定每个像素深度标签，从而导致显着的离散误差。我们第一次制定了完全可区分的序数回归，并以端到端的方式训练网络。这使我们能够在优化函数中包括边界和平滑度约束，从而导致平滑和边缘一致的深度图。还提出了一个新型的人均置信度图计算，以进行深度细化。对挑战基准的拟议模型的广泛评估揭示了其优于近期最新方法，无论是定量还是定性上。此外，我们使用挑战性现实生活图像的内部数据集展示了所提出的单个相机散景解决方案的实用性。
Single image depth estimation is a challenging problem. The current state-of-the-art method formulates the problem as that of ordinal regression. However, the formulation is not fully differentiable and depth maps are not generated in an end-to-end fashion. The method uses a naïve threshold strategy to determine per-pixel depth labels, which results in significant discretization errors. For the first time, we formulate a fully differentiable ordinal regression and train the network in end-to-end fashion. This enables us to include boundary and smoothness constraints in the optimization function, leading to smooth and edge-consistent depth maps. A novel per-pixel confidence map computation for depth refinement is also proposed. Extensive evaluation of the proposed model on challenging benchmarks reveals its superiority over recent state-of-the-art methods, both quantitatively and qualitatively. Additionally, we demonstrate practical utility of the proposed method for single camera bokeh solution using in-house dataset of challenging real-life images.
