Fast, High-Quality Hierarchical Depth-Map Super-Resolution

Abstract

The low spatial resolution of acquired depth maps is a major drawback of most RGBD sensors. However, there are many scenarios in which fast acquisition of high-resolution and high-quality depth maps would be desirable. One approach to achieve higher quality depth maps is through super-resolution. However, edge preservation is challenging, and artifacts such as depth confusion and blurring are easily introduced near boundaries. In view of this, we propose a method for fast, high-quality hierarchical depth-map super-resolution (HDS). In our method, a high-resolution RGB image is degraded layer by layer to guide the bilateral filtering of the depth map. To improve the upsampled depth map quality, we construct a feature-based bilateral filter (FBF) for the interpolation, by using the extracted RGB shallow and multi-layer features. To accelerate the process, we perform filtering only near depth boundaries and through matrix operations. We also propose an extension

Improved Upsampling Based Depth Image Super-Resolution Reconstruction

“…Chang et al [2] use potency guided upsampling and adaptive gradient fusion filters to enhance the erroneous depth images to refine the upsampled depth results. Qiao et al [3] construct a feature-based bilateral filter (FBF) for the interpolation, by using the extracted RGB shallow and multi-layer features to improve the upsampled depth image quality. Liu et al [4] puts forward a precise three-dimensional (3D) reconstruction method for uncooperative spacecraft based on a low-resolution time-of-flight (ToF) depth camera coupled with a high-resolution optical texture camera.…”