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In this paper, we address the problem of simultaneous relighting and novelview synthesis of a complex scene from multi-view images with a limited numberof light sources. We propose an analysis-synthesis approach called Relit-NeuLF.Following the recent neural 4D light field network (NeuLF), Relit-NeuLF firstleverages a two-plane light field representation to parameterize each ray in a4D coordinate system, enabling efficient learning and inference. Then, werecover the spatially-varying bidirectional reflectance distribution function(SVBRDF) of a 3D scene in a self-supervised manner. A DecomposeNet learns tomap each ray to its SVBRDF components: albedo, normal, and roughness. Based onthe decomposed BRDF components and conditioning light directions, a RenderNetlearns to synthesize the color of the ray. To self-supervise the SVBRDFdecomposition, we encourage the predicted ray color to be close to thephysically-based rendering result using the microfacet model. Comprehensiveexperiments demonstrate that the proposed method is efficient and effective onboth synthetic data and real-world human face data, and outperforms thestate-of-the-art results. We publicly released our code on GitHub. You can findit here: https://github.com/oppo-us-research/RelitNeuLF

Relit-NeuLF: Efficient Relighting and Novel View Synthesis via Neural 4D Light Field