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Depth Super Resolution by Rigid Body Self-Similarity in 3D_CVPR2013
Depth Super Resolution by Rigid Body Self-Similarity in 3D
Michael Horna?c?ek1,*, Christoph Rhemann2, Margrit Gelautz1, and Carsten Rother2
1Vienna University of Technology
2Microsoft Research Cambridge
Abstract
We tackle the problem of jointly increasing the spatial
resolution and apparent measurement accuracy of an input
low-resolution, noisy, and perhaps heavily quantized depth
map. In stark contrast to earlier work, we make no use
of ancillary data like a color image at the target resolu-
tion, multiple aligned depth maps, or a database of high-
resolution depth exemplars. Instead, we proceed by identi-
fying and merging patch correspondences within the input
depth map itself, exploiting patchwise scene self-similarity
across depth such as repetition of geometric primitives or
object symmetry. While the notion of ‘single-image’ super
resolution has successfully been applied in the context of
color and intensity images, we are to our knowledge the
first to present a tailored analogue for depth images. Rather
than reason in terms of patches of 2D pixels as others have
before us, our key contribution is to proceed by reason-
ing in terms of patches of 3D points, with matched patch
pairs related by a respective 6 DoF rigid body motion in
3D. In support of obtaining a dense correspondence field in
reasonable time, we introduce a new 3D variant of Patch-
Match. A third contribution is a simple, yet effective patch
upscaling and merging technique, which predicts sharp ob-
ject boundaries at the target resolution. We show that our
results are highly competitive with those of alternative tech-
niques leveraging even a color image at the target resolu-
tion or a database of high-resolution depth exemplars.
1. Introduction
With the advent of inexpensive 3D cameras like the
Microsoft Kinect, depth measurements are becoming in-
creasingly available for low-cost applications. Acquisitions
made by such consumer 3D cameras, however, remain af-
flicted by less than ideal attribu
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