We present an improved model for MRF-based depth upsampling, guided by image- as well as 3D surface normal features. By exploiting the underlying camera model we define a novel regularization term that implicitly evaluates the planarity of arbitrary oriented surfaces. Our method improves upsampling quality in scenes composed of predominantly planar surfaces, such as urban areas. We use a synthetic dataset to demonstrate that our approach outperforms recent methods that implement distance-based regularization terms. Finally, we validate our approach for mapping applications on our experimental vehicle.
@article{arxiv.1706.05999,
title = {Guided Depth Upsampling for Precise Mapping of Urban Environments},
author = {Sascha Wirges and Björn Roxin and Eike Rehder and Tilman Kühner and Martin Lauer},
journal= {arXiv preprint arXiv:1706.05999},
year = {2017}
}