Robust weighted least squares for guided depth upsampling
Wei Liu, Xiaogang Chen, Jie Yang, Qiang Wu · 2016
In this paper, we propose a new guided depth upsampling method denoted as Robust Weighted Least Squares (RWLS). Our work is inspired by the connection between the Weighted Least Squares (WLS) and the Auto Regressive (AR) model. By adopting a new robust penalty function to model the smoothness of the proposed model, we show that the proposed method performs much better in preserving sharp depth discontinuities than previous work. Through both mathematical analysis and experimental results, we show that our method has promising performance on handling the inconsistency between the guidance image and the depth map in both preserving sharp depth discontinuities and suppressing the texture copy artifacts.