Skullptor：借助多视图法线预测在数秒内实现高保真三维头部重建
Skullptor: High Fidelity 3D Head Reconstruction in Seconds with Multi-View Normal Prediction
把单目基础模型扩展成带跨视图注意力的多视图法线预测器，前馈输出几何一致法线，再作为强几何先验注入逆渲染优化恢复高频细节——这套「前馈先验 + 优化精修」的混合范式可直接迁移到人脸、人体、物体等少视图高保真重建任务。
原文依据 a multi-view surface normal prediction model that extends monocular foundation models with cross-view attention
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论从图像重建高保真三维头部几何对广泛应用至关重要，但现有方法存在根本性局限。传统摄影测量能获得极佳细节，却需要大规模相机阵列（25 至 200 个以上视角）、大量计算，并在胡须等困难区域依赖人工清理。近期的替代方案呈现出一种根本性的权衡：基础模型可实现高效的单图像重建，但缺乏精细几何细节；基于优化的方法保真度更高，却需要密集视角与高昂计算。我们以混合式方法弥合这一差距，融合两种范式的优势。我们的方法提出一个多视图表面法线预测模型，通过跨视图注意力扩展单目基础模型，在前馈推理中生成几何一致的法线。随后我们将这些预测作为强几何先验，纳入逆渲染优化框架以恢复高频表面细节。我们的方法优于最优的单图像与多视图方法，在降低相机需求与计算成本的同时，实现与密集视角摄影测量相当的高保真重建。
英文原文摘要 1264 字符
Reconstructing high-fidelity 3D head geometry from images is critical for a wide range of applications, yet existing methods face fundamental limitations. Traditional photogrammetry achieves exceptional detail but requires extensive camera arrays (25--200+ views), substantial computation, and manual cleanup in challenging areas like facial hair. Recent alternatives present a fundamental trade-off: foundation models enable efficient single-image reconstruction but lack fine geometric detail, while optimization-based methods achieve higher fidelity but require dense views and expensive computation. We bridge this gap with a hybrid approach that combines the strengths of both paradigms.Our method introduces a multi-view surface normal prediction model that extends monocular foundation models with cross-view attention to produce geometrically consistent normals in a feed-forward pass. We then leverage these predictions as strong geometric priors within an inverse rendering optimization framework to recover high-frequency surface details. Our approach outperforms state-of-the-art single-image and multi-view methods, achieving high-fidelity reconstruction on par with dense-view photogrammetry while reducing camera requirements and computational cost.