Mono3R: Exploiting Monocular Cues for Geometric 3D Reconstruction

• URL: http://arxiv.org/abs/2504.13419v1
• Date: Fri, 18 Apr 2025 02:33:12 GMT
• Title: Mono3R: Exploiting Monocular Cues for Geometric 3D Reconstruction
• Authors: Wenyu Li, Sidun Liu, Peng Qiao, Yong Dou,
• Abstract summary: We introduce a monocular-guided refinement module that integrates monocular geometric priors into multi-view reconstruction frameworks.<n>Our method achieves substantial improvements in both mutli-view camera pose estimation and point cloud accuracy.
• Score: 11.220655907305515
• License: http://creativecommons.org/licenses/by/4.0/
• 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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