Evaluation of CNN-Based Single-Image Depth Estimation Methods
Abstract
While an increasing interest in deep models for single-image depth estimation methods can be observed, established schemes for their evaluation are still limited. We propose a set of novel quality criteria, allowing for a more detailed analysis by focusing on specific characteristics of depth maps. In particular, we address the preservation of edges and planar regions, depth consistency, and absolute distance accuracy. In order to employ these metrics to evaluate and compare state-of-the-art single-image depth estimation approaches, we provide a new high-quality RGB-D dataset. We used a DSLR camera together with a laser scanner to acquire high-resolution images and highly accurate depth maps. Experimental results show the validity of our proposed evaluation protocol.
Cite
Text
Koch et al. "Evaluation of CNN-Based Single-Image Depth Estimation Methods." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11015-4_25Markdown
[Koch et al. "Evaluation of CNN-Based Single-Image Depth Estimation Methods." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/koch2018eccvw-evaluation/) doi:10.1007/978-3-030-11015-4_25BibTeX
@inproceedings{koch2018eccvw-evaluation,
title = {{Evaluation of CNN-Based Single-Image Depth Estimation Methods}},
author = {Koch, Tobias and Liebel, Lukas and Fraundorfer, Friedrich and Körner, Marco},
booktitle = {European Conference on Computer Vision Workshops},
year = {2018},
pages = {331-348},
doi = {10.1007/978-3-030-11015-4_25},
url = {https://mlanthology.org/eccvw/2018/koch2018eccvw-evaluation/}
}