Inaccuracy-Tolerant Sparse-to-Dense Depth Propagation for Semiautomatic 2D-to-3D Conversion

Abstract

Current semiautomatic 2D-to-3D methods assume that user input is perfectly accurate. However, it is difficult to get 100% accurate user scribbles and even small errors in the input will degrade the conversion quality. This paper addresses the issue with scribble confidence that considers color differences between labeled pixels and their neighbors. First, it counts the number of neighbors which have similar and different color values for each labeled pixels, respectively. The ratio between these two numbers at each labeled pixel is regarded as its scribble confidence. Second, the sparse-to-dense depth conversion is formulated as a confident optimization problem by introducing a confident weighting data cost term and the local and k-nearest depth consistent regularization terms. Finally, the dense depth-map is obtained by solving sparse linear equations. The proposed approach is compared with existing methods on several representative images. The experimental results demonstrate that the proposed method can tolerate some errors from use input and can reduce depth-map artifacts caused by inaccurate user input.