用图优化提升单目3D模型跨视角一致性与尺度对齐
MoRe: Monocular Geometry Refinement via Graph Optimization for Cross-View Consistency
- 基于帧间特征匹配构建图优化框架，局部平面近似保持结构
- 解决单目几何固有的尺度模糊问题，实现跨视角一致
- 无需训练，适用于稀疏视图渲染等场景
单目3D基础模型为感知任务提供了可扩展的解决方案，适用于更广泛的3D视觉应用。本文提出MoRe，一种无需训练的单目几何精修方法，旨在提升跨视角一致性并实现尺度对齐。通过帧间特征匹配建立对应关系，不采用简单的最小二乘优化，而是设计基于图的优化框架，利用单目基础模型估计的3D点和表面法向量进行局部平面近似。该方法有效缓解了单目几何先验固有的尺度模糊问题，同时保留了底层3D结构。实验表明，MoRe不仅提升3D重建质量，还显著改善了新视角合成效果，尤其在稀疏视图渲染场景下表现优异。
原文摘要 · Abstract (English)
Monocular 3D foundation models offer an extensible solution for perception tasks, making them attractive for broader 3D vision applications. In this paper, we propose MoRe, a training-free Monocular Geometry Refinement method designed to improve cross-view consistency and achieve scale alignment. To induce inter-frame relationships, our method employs feature matching between frames to establish correspondences. Rather than applying simple least squares optimization on these matched points, we formulate a graph-based optimization framework that performs local planar approximation using the estimated 3D points and surface normals estimated by monocular foundation models. This formulation addresses the scale ambiguity inherent in monocular geometric priors while preserving the underlying 3D structure. We further demonstrate that MoRe not only enhances 3D reconstruction but also improves novel view synthesis, particularly in sparse view rendering scenarios.
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