Pixelwise View Selection for Unstructured Multi-View Stereo
Abstract
This work presents a Multi-View Stereo system for robust and efficient dense modeling from unstructured image collections. Our core contributions are the joint estimation of depth and normal information, pixelwise view selection using photometric and geometric priors, and a multi-view geometric consistency term for the simultaneous refinement and image-based depth and normal fusion. Experiments on benchmarks and large-scale Internet photo collections demonstrate state-of-the-art performance in terms of accuracy, completeness, and efficiency.
Cite
Text
Schönberger et al. "Pixelwise View Selection for Unstructured Multi-View Stereo." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-46487-9_31Markdown
[Schönberger et al. "Pixelwise View Selection for Unstructured Multi-View Stereo." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/schonberger2016eccv-pixelwise/) doi:10.1007/978-3-319-46487-9_31BibTeX
@inproceedings{schonberger2016eccv-pixelwise,
title = {{Pixelwise View Selection for Unstructured Multi-View Stereo}},
author = {Schönberger, Johannes L. and Zheng, Enliang and Frahm, Jan-Michael and Pollefeys, Marc},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {501-518},
doi = {10.1007/978-3-319-46487-9_31},
url = {https://mlanthology.org/eccv/2016/schonberger2016eccv-pixelwise/}
}