Submitted by Fangjinhua Wang.

Submission data

Full namePatchmatchNet
DescriptionWe present PatchmatchNet, a novel and learnable cascade formulation of Patchmatch for high-resolution multi-view stereo. With high computation speed and low memory requirement, PatchmatchNet can process higher resolution imagery and is more suited to run on resource limited devices than competitors that employ 3D cost volume regularization. For the first time we introduce an iterative multi-scale Patchmatch in an end-to-end trainable architecture and improve the Patchmatch core algorithm with a novel and learned adaptive propagation and evaluation scheme for each iteration. Extensive experiments show a very competitive performance and generalization for our method on DTU, Tanks & Temples and ETH3D, but at a significantly higher efficiency than all existing top-performing models: at least two and a half times faster than state-of-the-art methods with twice less memory usage.
Parametersimage size: 2688*1792
Publication titlePatchmatchNet: Learned Multi-View Patchmatch Stereo
Publication authorsFangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys
Publication URLhttps://arxiv.org/abs/2012.01411
Programming language(s)python
HardwareIntel(R) Core(TM) i7-9700K CPU @ 3.60GHz, GeForce RTX 2080, RAM: 32GB
Source code or download URLhttps://github.com/FangjinhuaWang/PatchmatchNet
Submission creation date8 Aug, 2020
Last edited18 Mar, 2021

High-res multi-view results



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indooroutdoorcourty.delive.electrofacadekickermeadowofficepipesplaygr.reliefrelief.terraceterrai.
permissive64.2164.6563.6971.2968.6669.6351.7063.0453.7759.8358.3460.2368.5466.0675.5668.08

Low-res many-view results



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indooroutdoorlakesidesand boxstorage roomstorage room 2tunnel
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Low-res two-view results



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SLAM results



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