Abstract
Illuminant estimation is a fundamental problem in computational photography, as it enables the correction of color shifts induced by varying lighting conditions. While learning-based methods have demonstrated strong performance, their progress is hindered by the limited availability of large-scale datasets with accurate illuminant ground-truth. In this work, we propose a general and reusable pipeline to derive dense illuminant chromaticity maps from physically based 3D-rendered scenes. By repurposing an existing 3D scene collection, our approach enables the systematic generation of pixel-wise illuminant annotations under controlled lighting conditions, effectively lowering the barrier to data acquisition for learning-based illuminant estimation. Using this pipeline, we generate a large-scale synthetic set of 74,321 images, which we employ for pre-training both single- and multi-illuminant estimation models. Extensive experiments with state-of-the-art architectures show that synthetic pre-training consistently improves performance, with gains of up to 28% for single-illuminant estimation and up to 57% for multi-illuminant estimation, particularly in data-scarce regimes. These findings demonstrate that synthetic data generation pipelines offer an effective and scalable solution for the pre-training of illuminant estimation methods.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Cogo, L., Corti, G., Bianco, S., & Schettini, R. (2026). A General Pipeline for Dense Illuminant Estimation via Physically Based Synthetic Data. https://omanscience.com/en/articles/a-general-pipeline-for-dense-illuminant-estimation-via-physically-based-synthetic-data
MLA 9
Cogo, Luca, et al. "A General Pipeline for Dense Illuminant Estimation via Physically Based Synthetic Data." https://omanscience.com/en/articles/a-general-pipeline-for-dense-illuminant-estimation-via-physically-based-synthetic-data.
Chicago (author–date)
Cogo, Luca, Gianmarco Corti, Simone Bianco, and Raimondo Schettini. 2026. "A General Pipeline for Dense Illuminant Estimation via Physically Based Synthetic Data." https://omanscience.com/en/articles/a-general-pipeline-for-dense-illuminant-estimation-via-physically-based-synthetic-data.
Harvard
Cogo, L., Corti, G., Bianco, S. and Schettini, R. (2026) 'A General Pipeline for Dense Illuminant Estimation via Physically Based Synthetic Data', Available at: https://omanscience.com/en/articles/a-general-pipeline-for-dense-illuminant-estimation-via-physically-based-synthetic-data.
Vancouver
Cogo L, Corti G, Bianco S, Schettini R. A General Pipeline for Dense Illuminant Estimation via Physically Based Synthetic Data. https://omanscience.com/en/articles/a-general-pipeline-for-dense-illuminant-estimation-via-physically-based-synthetic-data
IEEE
L. Cogo, G. Corti, S. Bianco, and R. Schettini, "A General Pipeline for Dense Illuminant Estimation via Physically Based Synthetic Data," https://omanscience.com/en/articles/a-general-pipeline-for-dense-illuminant-estimation-via-physically-based-synthetic-data.