While deep learning has recently achieved great success on multi-view stereo
(MVS), limited training data makes the trained model hard to be generalized to
unseen scenarios. Compared with other computer vision tasks, it is rather
difficult to collect a large-scale MVS dataset as it requires expensive active
scanners and labor-intensive process to obtain ground truth 3D structures. In
this paper, we introduce BlendedMVS, a novel large-scale dataset, to provide
sufficient training ground truth for learning-based MVS. To create the dataset,
we apply a 3D reconstruction pipeline to recover high-quality textured meshes
from images of well-selected scenes. Then, we render these mesh models to color
images and depth maps. To introduce the ambient lighting information during
training, the rendered color images are further blended with the input images
to generate the training input. Our dataset contains over 17k high-resolution
images covering a variety of scenes, including cities, architectures,
sculptures and small objects. Extensive experiments demonstrate that BlendedMVS
endows the trained model with significantly better generalization ability
compared with other MVS datasets. The dataset and pretrained models are
available at https://github.com/YoYo000/BlendedMVS.
%0 Generic
%1 yao2019blendedmvs
%A Yao, Yao
%A Luo, Zixin
%A Li, Shiwei
%A Zhang, Jingyang
%A Ren, Yufan
%A Zhou, Lei
%A Fang, Tian
%A Quan, Long
%D 2019
%K 3d_reconstruction dataset deeplearning mvs neural_reconstruction
%T BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo
Networks
%U http://arxiv.org/abs/1911.10127
%X While deep learning has recently achieved great success on multi-view stereo
(MVS), limited training data makes the trained model hard to be generalized to
unseen scenarios. Compared with other computer vision tasks, it is rather
difficult to collect a large-scale MVS dataset as it requires expensive active
scanners and labor-intensive process to obtain ground truth 3D structures. In
this paper, we introduce BlendedMVS, a novel large-scale dataset, to provide
sufficient training ground truth for learning-based MVS. To create the dataset,
we apply a 3D reconstruction pipeline to recover high-quality textured meshes
from images of well-selected scenes. Then, we render these mesh models to color
images and depth maps. To introduce the ambient lighting information during
training, the rendered color images are further blended with the input images
to generate the training input. Our dataset contains over 17k high-resolution
images covering a variety of scenes, including cities, architectures,
sculptures and small objects. Extensive experiments demonstrate that BlendedMVS
endows the trained model with significantly better generalization ability
compared with other MVS datasets. The dataset and pretrained models are
available at https://github.com/YoYo000/BlendedMVS.
@misc{yao2019blendedmvs,
abstract = {While deep learning has recently achieved great success on multi-view stereo
(MVS), limited training data makes the trained model hard to be generalized to
unseen scenarios. Compared with other computer vision tasks, it is rather
difficult to collect a large-scale MVS dataset as it requires expensive active
scanners and labor-intensive process to obtain ground truth 3D structures. In
this paper, we introduce BlendedMVS, a novel large-scale dataset, to provide
sufficient training ground truth for learning-based MVS. To create the dataset,
we apply a 3D reconstruction pipeline to recover high-quality textured meshes
from images of well-selected scenes. Then, we render these mesh models to color
images and depth maps. To introduce the ambient lighting information during
training, the rendered color images are further blended with the input images
to generate the training input. Our dataset contains over 17k high-resolution
images covering a variety of scenes, including cities, architectures,
sculptures and small objects. Extensive experiments demonstrate that BlendedMVS
endows the trained model with significantly better generalization ability
compared with other MVS datasets. The dataset and pretrained models are
available at \url{https://github.com/YoYo000/BlendedMVS}.},
added-at = {2021-11-24T14:02:13.000+0100},
author = {Yao, Yao and Luo, Zixin and Li, Shiwei and Zhang, Jingyang and Ren, Yufan and Zhou, Lei and Fang, Tian and Quan, Long},
biburl = {https://www.bibsonomy.org/bibtex/2ba96cdf44a6fc9da0ed2497f4d846011/shuncheng.wu},
interhash = {2670198d8a35912c8ac2d6d40ee78838},
intrahash = {ba96cdf44a6fc9da0ed2497f4d846011},
keywords = {3d_reconstruction dataset deeplearning mvs neural_reconstruction},
note = {cite arxiv:1911.10127Comment: Accepted to CVPR2020},
timestamp = {2021-11-24T14:02:13.000+0100},
title = {BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo
Networks},
url = {http://arxiv.org/abs/1911.10127},
year = 2019
}