Object Color Relighting with Progressively Decreasing Information

Object color relighting, the process of predicting an object's colorimetric values under new lighting conditions, is a significant challenge in computational imaging and graphics. This technique has important applications in augmented reality, digital heritage, and e-commerce. In this paper, we address object color relighting under progressively decreasing information settings, ranging from full spectral knowledge to tristimulus-only input. Our framework systematically compares physics-based rendering, spectral reconstruction, and colorimetric mapping techniques across varying data regimes. Experiments span five benchmark reflectance datasets and eleven standard illuminants, with relighting accuracy assessed via ΔE00 metric. Results indicate that third-order polynomial regressions give good results when trained with small datasets, while neural spectral reconstruction achieves superior performance with large-scale training. Spectral methods also exhibit higher robustness to illuminant variability, emphasizing the value of intermediate spectral estimation in practical relighting scenarios.