FlashNormal：基于闪光与非闪光图像的详细表面法向量估计
FlashNormal: Detailed Surface Normal Estimation from Flash and No-Flash Images
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中文总结 AI 辅助
针对现有单图像法向量估计的细节恢复不足与模糊性问题，提出基于扩散模型的FlashNormal，利用闪光图像对的阴影变化与曲率引导增强，结合新数据集EvalFlash，实现法向量估计性能提升。
中文摘要 AI 辅助
高质量的表面法向量估计对于详细表面形状恢复和图像编辑至关重要。现有的基于单张图像的方法虽实用，但常难以恢复精细表面细节，且对固有的形状-反射率模糊性敏感。光度立体法虽能从不同光照下的图像实现高保真表面法向量估计，但其适用性受限于需要多光照采集设置。为此，我们提出FlashNormal，一种基于扩散模型的闪光/非闪光图像对表面法向量估计器。在保留现代智能手机高实用性的同时，该方法利用闪光诱导的阴影变化，采用曲率引导的细节增强策略，有效提升表面细节恢复能力并缓解形状-反射率模糊性。为评估所提方法，我们进一步构建EvalFlash，首个包含20个物体、带有表面法向量真值对齐的真实世界闪光/非闪光评估数据集，用于定量基准测试。大量实验表明，FlashNormal优于最先进的基于单张图像的方法，且在EvalFlash上相比基于闪光/非闪光的法向量估计方法有显著性能提升。
英文摘要
High-quality surface normal estimation is preferred for detailed surface shape recovery and image editing. Existing single image-based methods, though being a practical setup, often struggle to recover fine surface details and are sensitive to inherent shape-reflectance ambiguity. While photometric stereo achieves high-fidelity surface normal estimation from images under varying lights, its applicability is strictly limited by requiring a multi-illumination capture setup. To this end, we propose FlashNormal, a diffusion-based surface normal estimator from flash/no-flash image pairs. While retaining high practicability on modern smartphones, our proposal takes advantage of flash-induced shading variations, and leverages curvature-guided detail enhancement strategy, improving surface detail recovery and mitigating shape-reflectance ambiguity effectively. To evaluate our proposed method, we further present EvalFlash, the first real-world flash/no-flash evaluation dataset containing 20 objects aligned with ground-truth surface normals for quantitative benchmarking. Extensive experiments demonstrate the effectiveness of FlashNormal over state-of-the-art single image-based methods and show a significant out-performance over flash/no-flash-based normal estimation method on EvalFlash.
发表机构
- Beijing University of Posts and Telecommunications (BUPT)（北京邮电大学）
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