利用多光源信息提升真实场景的图像分解精度
MLI-NeRF: Multi-Light Intrinsic-Aware Neural Radiance Fields
- 通过多光源位置信息生成伪标签，无需真实标注
- 在合成与真实数据上均超越现有最佳方法
- 适用于图像编辑，适合做视觉重建的研究者
现有提取固有图像成分（如反射率和阴影）的方法主要依赖统计先验，仅适用于简单合成场景和孤立物体，在复杂真实世界数据上表现不佳。为此，我们提出MLI-NeRF，将多光源信息融入固有感知神经辐射场。利用不同光源位置提供的场景信息，补充多视角信息，生成反射率和阴影的伪标签，以指导无真值数据下的图像分解。该方法提供简单有效的监督信号，确保在多种场景类型下的鲁棒性。我们在合成与真实数据集上验证了该方法，性能优于现有最先进方法，并展示了其在各类图像编辑任务中的应用潜力。代码与数据已公开。
原文摘要 · Abstract (English)
Current methods for extracting intrinsic image components, such as reflectance and shading, primarily rely on statistical priors. These methods focus mainly on simple synthetic scenes and isolated objects and struggle to perform well on challenging real-world data. To address this issue, we propose MLI-NeRF, which integrates \textbf{M}ultiple \textbf{L}ight information in \textbf{I}ntrinsic-aware \textbf{Ne}ural \textbf{R}adiance \textbf{F}ields. By leveraging scene information provided by different light source positions complementing the multi-view information, we generate pseudo-label images for reflectance and shading to guide intrinsic image decomposition without the need for ground truth data. Our method introduces straightforward supervision for intrinsic component separation and ensures robustness across diverse scene types. We validate our approach on both synthetic and real-world datasets, outperforming existing state-of-the-art methods. Additionally, we demonstrate its applicability to various image editing tasks. The code and data are publicly available.
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