Dynamic Guidance for Depth Map Restoration

Ran Zhu, Shengju Yu, Xiaoyu Xu, Li Yu

Abstract: The guidance of color images greatly improves the restoration accuracy of the depth map. However, due to the incomplete texture structure consistency between the color image and the depth map, and the limitations of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L^{2}$</tex> based model, static guidance tends to cause texture copy artifacts and blurring depth discontinuities, which seriously deteriorates the quality of result. To tackle those problems, we propose a dynamic guidance model which can adaptively adjustment itself based on local smooth characteristics and continuously optimize in iteration. The proposed method can significantly alleviate the negative impact of the incomplete consistency and makes full use of the guidance information to restore fine texture at the same time. Moreover, a novel edge correction mechanism is designed to filter out incorrect depth information around boundaries, ensuring the correction of edges. In experiments, visual comparison proves that our method can alleviate texture copy artifacts and blurring depth discontinuities effectively. Quantitative results show that the proposed method restores high quality depth map with the lowest mean absolute error.