Reliev3R: Relieving Feed-forward Reconstruction from Multi-View Geometric Annotations
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911559482081280 |
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| author | Chen, Youyu Jiang, Junjun Luo, Yueru Jiang, Kui Liu, Xianming Yan, Xu Chen, Dave Zhenyu |
| author_facet | Chen, Youyu Jiang, Junjun Luo, Yueru Jiang, Kui Liu, Xianming Yan, Xu Chen, Dave Zhenyu |
| contents | With recent advances, Feed-forward Reconstruction Models (FFRMs) have demonstrated great potential in reconstruction quality and adaptiveness to multiple downstream tasks. However, the excessive reliance on multi-view geometric annotations, e.g. 3D point maps and camera poses, makes the fully-supervised training scheme of FFRMs difficult to scale up. In this paper, we propose Reliev3R, a weakly-supervised paradigm for training FFRMs from scratch without cost-prohibitive multi-view geometric annotations. Relieving the reliance on geometric sensory data and compute-exhaustive structure-from-motion preprocessing, our method draws 3D knowledge directly from monocular relative depths and image sparse correspondences given by zero-shot predictions of pretrained models. At the core of Reliev3R, we design an ambiguity-aware relative depth loss and a trigonometry-based reprojection loss to facilitate supervision for multi-view geometric consistency. Training from scratch with the less data, Reliev3R catches up with its fully-supervised sibling models, taking a step towards low-cost 3D reconstruction supervisions and scalable FFRMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_00548 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reliev3R: Relieving Feed-forward Reconstruction from Multi-View Geometric Annotations Chen, Youyu Jiang, Junjun Luo, Yueru Jiang, Kui Liu, Xianming Yan, Xu Chen, Dave Zhenyu Computer Vision and Pattern Recognition With recent advances, Feed-forward Reconstruction Models (FFRMs) have demonstrated great potential in reconstruction quality and adaptiveness to multiple downstream tasks. However, the excessive reliance on multi-view geometric annotations, e.g. 3D point maps and camera poses, makes the fully-supervised training scheme of FFRMs difficult to scale up. In this paper, we propose Reliev3R, a weakly-supervised paradigm for training FFRMs from scratch without cost-prohibitive multi-view geometric annotations. Relieving the reliance on geometric sensory data and compute-exhaustive structure-from-motion preprocessing, our method draws 3D knowledge directly from monocular relative depths and image sparse correspondences given by zero-shot predictions of pretrained models. At the core of Reliev3R, we design an ambiguity-aware relative depth loss and a trigonometry-based reprojection loss to facilitate supervision for multi-view geometric consistency. Training from scratch with the less data, Reliev3R catches up with its fully-supervised sibling models, taking a step towards low-cost 3D reconstruction supervisions and scalable FFRMs. |
| title | Reliev3R: Relieving Feed-forward Reconstruction from Multi-View Geometric Annotations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.00548 |