Multistep joint bilateral depth upsampling

Abstract

Depth maps are used in many applications, e.g. 3D television, stereo matching, segmentation, etc. Often, depth maps are available at a lower resolution compared to the corresponding image data. For these applications, depth maps must be upsampled to the image resolution. Recently, joint bilateral filters are proposed to upsample depth maps in a single step. In this solution, a high-resolution output depth is computed as a weighted average of surrounding low-resolution depth values, where the weight calculation depends on spatial distance function and intensity range function on the related image data. Compared to that, we present two novel ideas. Firstly, we apply anti-alias prefiltering on the high-resolution image to derive an image at the same low resolution as the input depth map. The upsample filter uses samples from both the high-resolution and the low-resolution images in the range term of the bilateral filter. Secondly, we propose to perform the upsampling in multiple stages, refining the resolution by a factor of 2×2 at each stage. We show experimental results on the consequences of the aliasing issue, and we apply our method to two use cases: a high quality ground-truth depth map and a real-time generated depth map of lower quality. For the first use case a relatively small filter footprint is applied; the second use case benefits from a substantially larger footprint. These experiments show that the dual image resolution range function alleviates the aliasing artifacts and therefore improves the temporal stability of the output depth map. On both use cases, we achieved comparable or better image quality with respect to upsampling with the joint bilateral filter in a single step. On the former use case, we feature a reduction of a factor of 5 in computational cost, whereas on the latter use case, the cost saving is a factor of 50.