Abstract
Recent works have shown that depth information can be obtained from Dual-Pixel (DP) sensors. A DP arrangement provides two views in a single shot, thus resembling a stereo image pair with a tiny baseline. However, the different point spread function (PSF) per view, as well as the small disparity range, makes the use of typical stereo matching algorithms problematic. To address the above shortcomings, we propose a Continuous Cost Aggregation (CCA) scheme within a semi-global matching framework that is able to provide accurate continuous disparities from DP images. The proposed algorithm fits parabolas to matching costs and aggregates parabola coefficients along image paths. The aggregation step is performed subject to a quadratic constraint that not only enforces the disparity smoothness but also maintains the quadratic form of the total costs. This gives rise to an inherently efficient disparity propagation scheme with a pixel-wise minimization in closed-form. Furthermore, the continuous form allows for a robust multi-scale aggregation that better compensates for the varying PSF. Experiments on DP data from both DSLR and phone cameras show that the proposed scheme attains state-of-the-art performance in DP disparity estimation.
Abstract (translated)
最近的工作表明，从双像素(DP)传感器可以获得深度信息。DP安排在一次拍摄中提供两个视角，因此类似于具有微小基线的立体图像对。然而，每个视角不同的点扩散函数(PSF)以及微小的差距范围使得使用典型的立体匹配算法存在问题。为了解决上述缺点，我们提出了一种连续成本聚合(CCA)方案，在一个半全球匹配框架内，它能够从DP图像中提供准确的连续差距。该提议算法将匹配成本与图像路径上的椭圆系数相 fit，并在整个图像路径上聚合椭圆系数。聚合步骤受到一个二阶约束，不仅强迫差距平滑，而且保持总成本的二阶形式。这产生了一个基于像素最小化的封闭形式的高效差距传播方案。此外，连续形式允许进行稳健的多尺度聚合，更好地补偿不断变化的PSF。从DSLR和手机相机收集的DP数据的实验表明，该提议方案在DP差距估计方面实现了先进的性能。
URL
https://arxiv.org/abs/2306.07921
