Problem
Research questions and friction points this paper is trying to address.
surface normal estimation
shape-reflectance ambiguity
single image-based methods
photometric stereo
FlashNormal: Detailed Surface Normal Estimation from Flash and No-Flash Images
🤖 AI Summary
为解决单图像法难以恢复精细表面细节的问题，提出FlashNormal方法，利用闪光/无闪光图像对和基于扩散的估计器，有效提高表面细节恢复。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
flash/no-flash image pairs
curvature-guided detail enhancement
surface normal estimation
shape-reflectance ambiguity
🔎 Similar Papers
No similar papers found.
