DEMVSNet: Denoising and depth inference for unstructured multi‐view stereo on noised images

DEMVSNet: Denoising and depth inference for unstructured multi‐view stereo on noised images

Abstract Most deep‐learning‐based multi‐view stereo series studies are concerned with improving the depth prediction accuracy of noise‐free images. However, it is difficult to obtain off‐the‐set clean images in practice and 3D convolutional neural networks require a lot of computing resources. To ma...

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Bibliographic Details
Main Authors: Jiawei Han, Xiaomei Chen, Yongtian Zhang, Weimin Hou, Zibo Hu
Format: Article
Language:English
Published: Wiley 2022-10-01
Series:IET Computer Vision
Subjects:
computer vision
neural net architecture
random noise
Online Access:https://doi.org/10.1049/cvi2.12102

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