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

• URL: http://arxiv.org/abs/2310.14642v1
• Date: Mon, 23 Oct 2023 07:29:51 GMT
• Title: Relit-NeuLF: Efficient Relighting and Novel View Synthesis via Neural 4D Light Field
• Authors: Zhong Li, Liangchen Song, Zhang Chen, Xiangyu Du, Lele Chen, Junsong Yuan, Yi Xu
• Abstract summary: We propose an analysis-synthesis approach called Relit-NeuLF. We first parameterize each ray in a 4D coordinate system, enabling efficient learning and inference. Comprehensive experiments demonstrate that the proposed method is efficient and effective on both synthetic data and real-world human face data.
• Score: 69.90548694719683
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we address the problem of simultaneous relighting and novel view synthesis of a complex scene from multi-view images with a limited number of light sources. We propose an analysis-synthesis approach called Relit-NeuLF. Following the recent neural 4D light field network (NeuLF), Relit-NeuLF first leverages a two-plane light field representation to parameterize each ray in a 4D coordinate system, enabling efficient learning and inference. Then, we recover the spatially-varying bidirectional reflectance distribution function (SVBRDF) of a 3D scene in a self-supervised manner. A DecomposeNet learns to map each ray to its SVBRDF components: albedo, normal, and roughness. Based on the decomposed BRDF components and conditioning light directions, a RenderNet learns to synthesize the color of the ray. To self-supervise the SVBRDF decomposition, we encourage the predicted ray color to be close to the physically-based rendering result using the microfacet model. Comprehensive experiments demonstrate that the proposed method is efficient and effective on both synthetic data and real-world human face data, and outperforms the state-of-the-art results. We publicly released our code on GitHub. You can find it here: https://github.com/oppo-us-research/RelitNeuLF

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