Abstract
Deep Neural Networks (DNNs) have the potential to improve the quality of image-based 3D reconstructions. However, the use of DNNs in the context of 3D reconstruction from large and high-resolution image datasets is still an open challenge, due to memory and computational constraints. We propose a pipeline which takes advantage of DNNs to improve the quality of 3D reconstructions while being able to handle large and high-resolution datasets. In particular, we propose a confidence prediction network explicitly tailored for Multi-View Stereo (MVS) and we use it for both depth map outlier filtering and depth map refinement within our pipeline, in order to improve the quality of the final 3D reconstructions. We train our confidence prediction network on (semi-)dense ground truth depth maps from publicly available real world MVS datasets. With extensive experiments on popular benchmarks, we show that our overall pipeline can produce state-of-the-art 3D reconstructions, both qualitatively and quantitatively.
Original language | English |
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Title of host publication | Proceedings - 2020 International Conference on 3D Vision, 3DV 2020 |
Publisher | IEEEXplore |
Pages | 404-413 |
Number of pages | 10 |
ISBN (Electronic) | 9781728181288 |
DOIs | |
Publication status | Published - 25 Nov 2020 |
Event | 8th International Conference on 3D Vision: 3DV 2020 - Online, Fukuoka, Virtual, Japan Duration: 25 Nov 2020 → 28 Nov 2020 http://3dv2020.dgcv.nii.ac.jp |
Publication series
Name | Proceedings - 2020 International Conference on 3D Vision, 3DV 2020 |
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Conference
Conference | 8th International Conference on 3D Vision |
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Abbreviated title | 3DV |
Country/Territory | Japan |
City | Fukuoka, Virtual |
Period | 25/11/20 → 28/11/20 |
Internet address |
Keywords
- 3D Reconstruction
- Confidence Prediction
- Multi View Stereo
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition