Sparse-to-dense depth conversion is an important task in semi-automatic 2D-to-3D conversion. Existing methods do not handle structural difference between texture image and depth map

and the error-tolerance of 2D-to-3D is not considered. Inspired by compressive sensing studies

we address these problems in an optimization framework via

L

1

norm. First

data term is built with

L

1

norm to measure the fidelity between estimated depth and user ass

igned depth. Second

local regularized term is defined by using feature weighted

L

1

norm to measure difference between local neighboring pixels. Third

super-pixels are generated from input image and global regularized term is introduced by using feature weighted

L

1

norm to measure difference between representative pixels from these super-pixels. Then

the energy function for sparse-to-dense depth conversion is defined based on the data term

local regularized term and global regularized term. The split Bregman algorithm is used to solve the energy. Experimental comparisons with optimization based interpolation

random-walks

hybrid graph-cuts and random-walks

soft segmentation constrained interpolation and nonlocal random-walks show that our method demonstrates significant advantages over hole and ghosting artifacts for viewpoint synthesis. The PSNR is improved by more than 0.9 dB compared with these methods when user assigns error depth.