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 artefacts 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 of our HDS model to a Classification-based Hierarchical Depth-map Super-resolution (C-HDS) model, where a context-aware trilateral filter reduces the contributions of unreliable neighbours to the current missing depth location. Experimental results show that the proposed method is significantly faster than existing methods for generating high-resolution depth maps, while also significantly improving depth quality compared to the current state-of-the-art approaches, especially for large-scale 16X super-resolution.
|Number of pages||10|
|Publication status||Published - 20 Oct 2021|
|Event||29th ACM International Conference on Multimedia, MM 2021 - |
Duration: 20 Oct 2021 → 24 Oct 2021
|Conference||29th ACM International Conference on Multimedia, MM 2021|
|Period||20/10/21 → 24/10/21|