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The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.

NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video

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Sun, Jiaming, Xie, Yiming, Chen, Linghao, Zhou, Xiaowei, Bao, Hujun·2021·2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Abstract

We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video. Unlike previous methods that estimate single-view depth maps separately on each key-frame and fuse them later, we propose to directly reconstruct local surfaces represented as sparse TSDF volumes for each video fragment sequentially by a neural network. A learning-based TSDF fusion module based on gated recurrent units is used to guide the network to fuse features from previous fragments. This de-sign allows the network to capture local smoothness prior and global shape prior of 3D surfaces when sequentially reconstructing the surfaces, resulting in accurate, coherent, and real-time surface reconstruction. The experiments on ScanNet and 7-Scenes datasets show that our system outperforms state-of-the-art methods in terms of both ac-curacy and speed. To the best of our knowledge, this is the first learning-based system that is able to reconstruct dense coherent 3D geometry in real-time. Code is available at the project page: https://zju3dv.github.io/neuralrecon/.

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Metadata

Authors
Sun, J., Xie, Y., Chen, L., Zhou, X., & Bao, H.
Venue
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2021-06
Pages
15593-15602
ISSN
1063-6919 (ISSN-L)
OpenAlex
W3172610252
Cited by
313
References
54
FWCI
44.49
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video. https://app.cochat.ai/papers/10.1109/cvpr46437.2021.01534
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Sun, J., Xie, Y., Chen, L., Zhou, X., & Bao, H. (2021). NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr46437.2021.01534

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