提出事件间隔轮廓方法，提升事件相机在复杂光照下的表面法向恢复鲁棒性。
PS-EIP: Robust Photometric Stereo Based on Event Interval Profile
- 基于事件间隔的时间序列轮廓建模，利用连续性增强稳定性。
- 通过轮廓形状检测异常点，有效抑制阴影和镜面反射干扰。
- 无需深度学习，实测性能优于现有基于网络的方法。
近期提出的事件相机光度立体法（EventPS）可从对数朗伯反射变化触发的事件中恢复表面法向，但其将每个事件间隔独立处理，易受噪声、阴影及非朗伯反射影响。本文提出基于事件间隔轮廓的光度立体方法（PS-EIP），通过分析事件间隔的时间序列轮廓，并引入基于轮廓形状的异常检测机制，提升了对阴影和镜面反射等异常值的鲁棒性。实验使用3D打印物体的真实事件数据验证，PS-EIP在不依赖深度学习的情况下，显著优于事件相机光度立体的深度学习变体EventPS-FCN。
原文摘要 · Abstract (English)
Recently, the energy-efficient photometric stereo method using an event camera has been proposed to recover surface normals from events triggered by changes in logarithmic Lambertian reflections under a moving directional light source. However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections. This paper proposes Photometric Stereo based on Event Interval Profile (PS-EIP), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals. By exploiting the continuity of the profile and introducing an outlier detection method based on profile shape, our approach enhances robustness against outliers from shadows and specular reflections. Experiments using real event data from 3D-printed objects demonstrate that PS-EIP significantly improves robustness to outliers compared to EventPS's deep-learning variant, EventPS-FCN, without relying on deep learning.
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