用闪光灯与不闪光图像对，提升手机拍摄的表面法向估计精度。
FlashNormal: Detailed Surface Normal Estimation from Flash and No-Flash Images
- 基于扩散模型，利用闪光灯引起的明暗变化提取表面细节。
- 在真实数据集EvalFlash上比现有方法误差降低18.7%。
- 适合移动端三维重建与图像编辑场景使用。
高质量的表面法向估计对于精细表面形状恢复和图像编辑至关重要。现有单图方法虽实用，但难以恢复细小表面特征，且易受形状-反射率模糊性影响。而光度立体法虽能实现高保真估计，却需多光照采集，适用受限。为此，我们提出FlashNormal，一种基于闪光/非闪光图像对的扩散模型表面法向估计算法。该方法在保留现代智能手机实用性的前提下，利用闪光引起的阴影变化，并引入曲率引导的细节增强策略，有效提升了表面细节恢复能力并缓解了形状-反射率模糊问题。为进一步评估，我们构建了首个真实世界闪光/非闪光图像评估数据集EvalFlash，包含20个物体及对应的地面真值表面法向，支持定量比较。大量实验表明，FlashNormal在性能上显著优于现有单图方法，并在EvalFlash数据集上超越已有闪光/非闪光方法。
原文摘要 · Abstract (English)
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.
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