4 accepted papers
In this paper, we propose a novel real-time disparity refinement method that enables precise structure perception. We construct a compact full-resolution cost volume from residuals around the initial disparity and adaptively eliminate redundant information on a per-pixel basis by leveraging the conf
Depth completion is one of the crucial methods to estimate dense depth information of surrounding environments for various real-world applications such as autonomous driving, robotics, and augmented reality. In these real-world applications, it is strictly required for a depth completion model to ac
While deep neural networks show great performance on fitting to the training distribution, improving the networks' generalization performance to the test distribution and robustness to the sensitivity to input perturbations still remain as a challenge. Although a number of mixup based augmentation s…