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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.11845 |
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| _version_ | 1866917986746499072 |
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| author | Zhu, Jie Peng, Bo Zhang, Zhe Liu, Bingzheng Lei, Jianjun |
| author_facet | Zhu, Jie Peng, Bo Zhang, Zhe Liu, Bingzheng Lei, Jianjun |
| contents | Learning-based Multi-View Stereo (MVS) methods have made remarkable progress in recent years. However, how to effectively train the network without using real-world labels remains a challenging problem. In this paper, driven by the recent advancements of vision foundation models, a novel method termed DFM-MVS, is proposed to leverage the depth foundation model to generate the effective depth prior, so as to boost MVS in the absence of real-world labels. Specifically, a depth prior-based pseudo-supervised training mechanism is developed to simulate realistic stereo correspondences using the generated depth prior, thereby constructing effective supervision for the MVS network. Besides, a depth prior-guided error correction strategy is presented to leverage the depth prior as guidance to mitigate the error propagation problem inherent in the widely-used coarse-to-fine network structure. Experimental results on DTU and Tanks & Temples datasets demonstrate that the proposed DFM-MVS significantly outperforms existing MVS methods without using real-world labels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11845 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Boosting Multi-View Stereo with Depth Foundation Model in the Absence of Real-World Labels Zhu, Jie Peng, Bo Zhang, Zhe Liu, Bingzheng Lei, Jianjun Computer Vision and Pattern Recognition Learning-based Multi-View Stereo (MVS) methods have made remarkable progress in recent years. However, how to effectively train the network without using real-world labels remains a challenging problem. In this paper, driven by the recent advancements of vision foundation models, a novel method termed DFM-MVS, is proposed to leverage the depth foundation model to generate the effective depth prior, so as to boost MVS in the absence of real-world labels. Specifically, a depth prior-based pseudo-supervised training mechanism is developed to simulate realistic stereo correspondences using the generated depth prior, thereby constructing effective supervision for the MVS network. Besides, a depth prior-guided error correction strategy is presented to leverage the depth prior as guidance to mitigate the error propagation problem inherent in the widely-used coarse-to-fine network structure. Experimental results on DTU and Tanks & Temples datasets demonstrate that the proposed DFM-MVS significantly outperforms existing MVS methods without using real-world labels. |
| title | Boosting Multi-View Stereo with Depth Foundation Model in the Absence of Real-World Labels |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.11845 |