Event Fusion Photometric Stereo Network
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We introduce a novel method to estimate surface normal of an object in an ambient light environment using RGB and event cameras. Modern photometric stereo methods rely on RGB cameras in a darkroom to avoid ambient illumination. To alleviate the limitations of using an RGB camera in a darkroom setting, we utilize an event camera with high dynamic range and low latency by capturing essential light information. This is the first study to use event cameras for photometric stereo in continuous light sources and ambient light environments. Additionally, we curate a new photometric stereo dataset captured by RGB and event cameras under various ambient lights. Our proposed framework, Event Fusion Photometric Stereo Network (EFPS-Net), estimates surface normals using RGB frames and event signals. EFPS-Net outperforms state-of-the-art methods on a real-world dataset with ambient lights, demonstrating the effectiveness of incorporating additional modalities to alleviate limitations caused by ambient illumination.
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