Abstract
Depth maps play an important role in the representation of 3D information. They are often simultaneously acquired with color images; however, their resolution is significantly lower than that of color images owing to hardware limitations. In this paper, we propose a novel approach to upsample depth maps by using geometric deformation instead of pixel value refinement, which is employed in a majority of existing methods. This approach, known as grid warping, displaces the position of blurred pixels around the edge towards the center of the edge. The displacement vector for warping is obtained from an analysis of the corresponding high-resolution color image. Furthermore, we propose an edge signal and displacement vector modeling for a more effective analysis. The experimental results show that the proposed method significantly improves the quantitative and visual performance, as compared to state-of-the-art methods. The source codes of the proposed method will be available at https://github.com/yym064/DeepGridWarp.
Highlights
O WING to the developments in 3D technologies, considerable attempts have been made to apply 3D technologies to various types of applications, including robotics and advanced driver assistance systems [8], [35]
Similar to the non-local mean filter, this term enables the contribution from faraway pixels during processing. Another Markov random field (MRF) formulation was suggested by Lu et al [25], wherein the truncated absolute difference between the estimated and the input depth value is employed for depth map upsampling
It is observed that the proposed method achieves the best performance for almost all test cases in terms of the both root mean square error (RMSE) and mean absolute difference error (MAE)
Summary
O WING to the developments in 3D technologies, considerable attempts have been made to apply 3D technologies to various types of applications, including robotics and advanced driver assistance systems [8], [35]. A popular approach to address this problem is joint filtering, i.e., the upsampling filter is derived using the depth map as well as its corresponding color image. A filter is designed to transfer meaningful structural information in the color edge to the depth map These approaches commonly result in two problems during information transfer. We classify the existing upsampling methods into the following three categories: model-based kernel filtering, optimization problem, and deep learning approaches. Similar to the non-local mean filter, this term enables the contribution from faraway pixels during processing Another MRF formulation was suggested by Lu et al [25], wherein the truncated absolute difference between the estimated and the input depth value is employed for depth map upsampling.
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