Discontinuous and Smooth Depth Completion with Binary Anisotropic Diffusion Tensor
This work addresses depth completion for computer vision applications, offering an incremental improvement by enhancing discontinuity preservation to reduce artifacts.
The paper tackles the problem of unsupervised real-time dense depth completion from sparse depth maps guided by a single image, achieving improved accuracy over previous methods while preserving object discontinuities to produce visually plausible point clouds.
We propose an unsupervised real-time dense depth completion from a sparse depth map guided by a single image. Our method generates a smooth depth map while preserving discontinuity between different objects. Our key idea is a Binary Anisotropic Diffusion Tensor (B-ADT) which can completely eliminate smoothness constraint at intended positions and directions by applying it to variational regularization. We also propose an Image-guided Nearest Neighbor Search (IGNNS) to derive a piecewise constant depth map which is used for B-ADT derivation and in the data term of the variational energy. Our experiments show that our method can outperform previous unsupervised and semi-supervised depth completion methods in terms of accuracy. Moreover, since our resulting depth map preserves the discontinuity between objects, the result can be converted to a visually plausible point cloud. This is remarkable since previous methods generate unnatural surface-like artifacts between discontinuous objects.