Sparse-to-Dense Depth Estimation in Videos via High-Dimensional Tensor Voting
Due to the popularity of 3D videos, 2D-to-3D video conversion has become a hot research topic for the past few years. The most critical issue in 3D video synthesis is the estimation of depth maps for the video frames. Numerous efforts have been devoted in fully automatic and semi-automatic depth estimation approaches, although the discontinuity of depth field and the ambiguity of motion boundary are still the main challenges in depth estimation. This paper proposes a semi-automatic structure-aware sparse-to-dense depth estimation method, which leverages the tensor voting at two different levels to propagate depth across frames. In the first level, a 4D tensor voting is performed to remove outliers caused by inaccurate motion estimation. Noticing that the 4D tensors of correctly matched points should lie on the smooth layer in the manifold, we utilize the variety saliency defined by the eigen-system of the tensor for outlier removal. In the second level, a high-dimensional tensor voting algorithm, incorporating spatial location, motion, and color into the tensor representation, is devised to propagate the depth from the sparse points to the entire image domain. By projecting the input feature into the tangent space, the relation between the location, motion, color, and the depth can be established by voting process. Extensive experiments on public data set validate the effectiveness of the proposed method in comparison with state-of-the-art depth estimation approaches.