计算机科学
推论
图像扭曲
一般化
深度图
特征(语言学)
可微函数
人工智能
深度学习
公制(单位)
计算机视觉
模式识别(心理学)
图像(数学)
数学
数学分析
哲学
语言学
经济
运营管理
作者
Yao Yao,Zixin Luo,Shiwei Li,Tian Fang,Long Quan
标识
DOI:10.1007/978-3-030-01237-3_47
摘要
We present an end-to-end deep learning architecture for depth map inference from multi-view images. In the network, we first extract deep visual image features, and then build the 3D cost volume upon the reference camera frustum via the differentiable homography warping. Next, we apply 3D convolutions to regularize and regress the initial depth map, which is then refined with the reference image to generate the final output. Our framework flexibly adapts arbitrary N-view inputs using a variance-based cost metric that maps multiple features into one cost feature. The proposed MVSNet is demonstrated on the large-scale indoor DTU dataset. With simple post-processing, our method not only significantly outperforms previous state-of-the-arts, but also is several times faster in runtime. We also evaluate MVSNet on the complex outdoor Tanks and Temples dataset, where our method ranks first before April 18, 2018 without any fine-tuning, showing the strong generalization ability of MVSNet.
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