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arXiv · 2504.13419

Mono3R: Exploiting Monocular Cues for Geometric 3D Reconstruction

Wenyu Li·Sidun Liu·Peng Qiao·Yong Dou
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Abstract

Recent advances in data-driven geometric multi-view 3D reconstruction foundation models (e.g., DUSt3R) have shown remarkable performance across various 3D vision tasks, facilitated by the release of large-scale, high-quality 3D datasets. However, as we observed, constrained by their matching-based principles, the reconstruction quality of existing models suffers significant degradation in challenging regions with limited matching cues, particularly in weakly textured areas and low-light conditions. To mitigate these limitations, we propose to harness the inherent robustness of monocular geometry estimation to compensate for the inherent shortcomings of matching-based methods. Specifically, we introduce a monocular-guided refinement module that integrates monocular geometric priors into multi-view reconstruction frameworks. This integration substantially enhances the robustness of multi-view reconstruction systems, leading to high-quality feed-forward reconstructions. Comprehensive experiments across multiple benchmarks demonstrate that our method achieves substantial improvements in both mutli-view camera pose estimation and point cloud accuracy.

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Wenyu Li, Sidun Liu, Peng Qiao, Yong Dou. 2025-04-18. Mono3R: Exploiting Monocular Cues for Geometric 3D Reconstruction. https://arxiv.org/abs/2504.13419

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