The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Recurrent MVSNet for High-Resolution Multi-View Stereo Depth Inference
Open accessAbstract
Deep learning has recently demonstrated its excellent performance for multi-view stereo (MVS). However, one major limitation of current learned MVS approaches is the scalability: the memory-consuming cost volume regularization makes the learned MVS hard to be applied to high-resolution scenes. In this paper, we introduce a scalable multi-view stereo framework based on the recurrent neural network. Instead of regularizing the entire 3D cost volume in one go, the proposed Recurrent Multi-view Stereo Network (R-MVSNet) sequentially regularizes the 2D cost maps along the depth direction via the gated recurrent unit (GRU). This reduces dramatically the memory consumption and makes high-resolution reconstruction feasible. We first show the state-of-the-art performance achieved by the proposed R-MVSNet on the recent MVS benchmarks. Then, we further demonstrate the scalability of the proposed method on several large-scale scenarios, where previous learned approaches often fail due to the memory constraint. Code is available at https://github.com/YoYo000/MVSNet.
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Furukawa, Y., & Ponce, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.161 · Cited by 3,019
Pixelwise View Selection for Unstructured Multi-View Stereo
Schönberger, J. L., Zheng, E., Frahm, J.-M., & Pollefeys, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46487-9_31 · Cited by 2,315
Pyramid Stereo Matching Network
Chang, J.-R., & Chen, Y.-S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00567 · Cited by 1,807
Tanks and temples: benchmarking large-scale scene reconstruction
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MVSNet: Depth Inference for Unstructured Multi-view Stereo
Yao, Y., Luo, Z., Li, S., Fang, T., & Quan, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01237-3_47 · Cited by 1,455
Self-citationAdaptive support-weight approach for correspondence search
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BlendedMVS: A Large-Scale Dataset for Generalized Multi-View Stereo Networks
Yao, Y., Luo, Z., Li, S., Zhang, J., Ren, Y., Zhou, L., Fang, T., & Quan, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00186 · Cited by 478
Self-citationPatchmatchNet: Learned Multi-View Patchmatch Stereo
Wang, F., Galliani, S., Vogel, C., Speciale, P., & Pollefeys, M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01397 · Cited by 420
Metadata
- Authors
- Yao, Y., Luo, Z., Li, S., Shen, T., Fang, T., & Quan, L.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 5520-5529
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2926429807
- Cited by
- 666
- References
- 30
- FWCI
- 19.57
- Type
- conference paper
- Language
- en
- Open access
- green
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- Yao, Y., Luo, Z., Li, S., Shen, T., Fang, T., & Quan, L. (2019). Recurrent MVSNet for High-Resolution Multi-View Stereo Depth Inference. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00567
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