M3VSNET: Unsupervised Multi-Metric Multi-View Stereo Network

Baichuan Huang, Hongwei Yi, Can Huang, Yijia He, Jingbin Liu, Xiao Liu

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceedingPeer review

Sammanfattning

The present Multi-view stereo (MVS) methods with supervised learning-based networks have an impressive performance comparing with traditional MVS methods. However, the ground-truth depth maps for training are hard to be obtained and are within limited kinds of scenarios. In this paper, we propose a novel unsupervised multi-metric MVS network, named M 3 VSNet, for dense point cloud reconstruction without any supervision. To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function to learn the inherent constraints from different perspectives of matching correspondences. Besides, we also incorporate the normal-depth consistency in the 3D point cloud format to improve the accuracy and continuity of the estimated depth maps. Experimental results show that M 3 VSNet establishes the state-of-the-arts unsupervised method and achieves better performance than previous supervised MVSNet on the DTU dataset and demonstrates the powerful generalization ability on the Tanks & Temples benchmark with effective improvement.
Originalspråkengelska
Titel på värdpublikationProceeding 2021 IEEE International Conference on Image Processing (ICIP)
FörlagIEEE - Institute of Electrical and Electronics Engineers Inc.
Sidor3163-3167
ISBN (elektroniskt)978-1-6654-4115-5
DOI
StatusPublished - 2021 sep. 19
Externt publiceradJa
Evenemang2021 IEEE International Conference on Image Processing (ICIP) - Anchorage, AK, USA
Varaktighet: 2021 sep. 192021 sep. 22

Konferens

Konferens2021 IEEE International Conference on Image Processing (ICIP)
Period2021/09/192021/09/22

Ämnesklassifikation (UKÄ)

  • Datorseende och robotik (autonoma system)

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