Methods › Computer Vision › Meshing › NeuralRecon
NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video
NeuralRecon
Introduced by Jiaming Sun et al. in NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
NeuralRecon is a framework 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, NeuralRecon proposes 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 design allows the network to capture local smoothness prior and global shape prior of 3D surfaces.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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DG-Recon: Depth-Guided Neural 3D Scene Reconstruction 1 Jan 2023 · 0 repositories
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NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video 1 Apr 2021 · 3 repositories · arXiv:2104.00681Syntology ran 2 of 7 samples · 5 unverified
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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