Event-ID: Intrinsic Decomposition Using an Event Camera

Reconstructing 3D scenes from multi-view images is challenging, especially in adverse conditions. We propose a novel event-based intrinsic decomposition framework that leverages events and images for stable decomposition under extreme scenarios. Our method is based on two observations: event cameras maintain good imaging quality, and events from different viewpoints exhibit similarity in diffuse regions while varying in specular regions. We establish an event-based reflectance model and introduce an event-based warping method to extract specular clues. Our two-part framework constructs a radiance field and decomposes the scene into normal, material, and lighting. Experimental results demonstrate superior performance compared to state-of-the-art methods. Our contributions include an event-based reflectance model, event warping-based consistency learning, and a framework for event-based intrinsic decomposition.