Continuous Cost Aggregation for Dual-Pixel Disparity Extraction
Sagi Monin Sagi Katz Georgios Evangelidis
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.