Accurate 3D objects relighting in diverse unseen environments is crucial for realistic virtual object placement. Due to the albedo-lighting ambiguity, existing methods often fall short in producing faithful relights. Without proper constraints, observed training views can be explained by numerous combinations of lighting and material attributes, lacking physical correspondence with the actual environment maps used for relighting. In this work, we present ReCap, treating cross-environment captures as multi-task target to provide the missing supervision that cuts through the entanglement. Specifically, ReCap jointly optimizes multiple lighting representations that share a common set of material attributes. This naturally harmonizes a coherent set of lighting representations around the mutual material attributes, exploiting commonalities and differences across varied object appearances. Such coherence enables physically sound lighting reconstruction and robust material estimation - both essential for accurate relighting. Together with a streamlined shading function and effective post-processing, ReCap outperforms the leading competitor by 3.4 dB in PSNR on an expanded relighting benchmark.
在多样且未知的环境中实现准确的3D对象重光照（relighting）对于虚拟对象的真实感放置至关重要。然而，由于反照率（albedo）与光照的模糊性，现有方法往往无法生成真实可信的重光照效果。缺乏适当约束时，训练视图可能被解释为多种光照与材质属性的组合，这些组合与实际用于重光照的环境光图缺乏物理对应关系。 在本研究中，我们提出了 ReCap 方法，通过将跨环境捕捉任务视为多任务目标，提供缺失的监督信号，解开光照与材质之间的纠缠。具体而言，ReCap 同时优化多个光照表示，并共享一组共同的材质属性。这样的设计自然协调了一组围绕共同材质属性的连贯光照表示，充分利用对象在不同外观下的共性和差异。 这种连贯性支持物理合理的光照重建和稳健的材质估计，而这两者对于实现精确的重光照至关重要。结合精简的着色函数与高效的后处理，ReCap 在扩展的重光照基准上，较领先方法的峰值信噪比（PSNR）提升了3.4 dB，显著优于现有技术。