Data-Driven Depth Map Refinement via Multi-Scale Sparse Representation

HyeokHyen Kwon, Yu-Wing Tai, Stephen Lin; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 159-167

Abstract


Depth maps captured by consumer-level depth cameras such as Kinect are usually degraded by noise, missing values, and quantization. In this paper, we present a data-driven approach for refining degraded RAW depth maps that are coupled with an RGB image. The key idea of our approach is to take advantage of a training set of high-quality depth data and transfer its information to the RAW depth map through multi-scale dictionary learning. Utilizing a sparse representation, our method learns a dictionary of geometric primitives which captures the correlation between high-quality mesh data, RAW depth maps and RGB images. The dictionary is learned and applied in a manner that accounts for various practical issues that arise in dictionary-based depth refinement. Compared to previous approaches that only utilize the correlation between RAW depth maps and RGB images, our method produces improved depth maps without over-smoothing. Since our approach is data driven, the refinement can be targeted to a specific class of objects by employing a corresponding training set. In our experiments, we show that this leads to additional improvements in recovering depth maps of human faces.

Related Material


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[bibtex]
@InProceedings{Kwon_2015_CVPR,
author = {Kwon, HyeokHyen and Tai, Yu-Wing and Lin, Stephen},
title = {Data-Driven Depth Map Refinement via Multi-Scale Sparse Representation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}