DeepMVS: Learning Multi-View Stereopsis
Abstract
We present DeepMVS, a deep convolutional neural network (ConvNet) for multi-view stereo reconstruction. Taking an arbitrary number of posed images as input, we first produce a set of plane-sweep volumes and use the proposed DeepMVS network to predict high-quality disparity maps. The key contributions that enable these results are (1) supervised pretraining on a photorealistic synthetic dataset, (2) an effective method for aggregating information across a set of unordered images, and (3) integrating multi-layer feature activations from the pre-trained VGG-19 network. We validate the efficacy of DeepMVS using the ETH3D Benchmark. Our results show that DeepMVS compares favorably against state-of-the-art conventional MVS algorithms and other ConvNet based methods, particularly for near-textureless regions and thin structures.
Cite
Text
Huang et al. "DeepMVS: Learning Multi-View Stereopsis." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00298Markdown
[Huang et al. "DeepMVS: Learning Multi-View Stereopsis." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/huang2018cvpr-deepmvs/) doi:10.1109/CVPR.2018.00298BibTeX
@inproceedings{huang2018cvpr-deepmvs,
title = {{DeepMVS: Learning Multi-View Stereopsis}},
author = {Huang, Po-Han and Matzen, Kevin and Kopf, Johannes and Ahuja, Narendra and Huang, Jia-Bin},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2018},
doi = {10.1109/CVPR.2018.00298},
url = {https://mlanthology.org/cvpr/2018/huang2018cvpr-deepmvs/}
}