PS-EIP: Robust Photometric Stereo Based on Event Interval Profile
This work addresses robustness issues in photometric stereo for event cameras, which is an incremental improvement over prior methods.
The paper tackles the problem of recovering surface normals from event camera data in photometric stereo, which is sensitive to noise and non-Lambertian effects, by proposing PS-EIP, a method that uses event interval profiles and outlier detection to improve robustness. Experiments on real event data show that PS-EIP significantly enhances robustness compared to a deep-learning baseline, EventPS-FCN, without using deep learning.
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.