Texture-free large-area depth recovery for planar surfaces

This paper presents a texture-free depth enhancement method for large-area depth recovery. The proposed algorithm identifies a large-area depth missing region, and iteratively segments its contour by setting different initial pixels in each iteration. Coordinate transformation is used to analyze the distribution of each contour segment. By examining distributions of all contour segments, statistical histogram analysis is applied in our approach to select contour pixels. Then, selected pixels are projected into the world coordinate system, and multiple linear regression is utilized for surface function approximation. Missing depth values of a large-area depth missing region can be recovered with guidance of the approximated surface function. Quantitative and qualitative evaluations over state-of-the-art depth enhancement methods demonstrate the effectiveness and superiority of our method. Being texture-free, the proposed method has the flexibility of being merged into traditional depth enhancement methods.