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Deep Illumination–Guided Light Probe Placement

Deep Illumination–Guided Light Probe Placement

A. Tarasidis, A. A. Vasilakis and I. Fudos

Eurographics Posters, 2026

This work introduces a neural-network-based method to automatically place light probes efficiently under diverse lighting conditions, preserving the scene’s indirect illumination. It delivers probe layouts much faster than traditional methods while maintaining high illumination quality.

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Abstract

This work proposes an automated learning-based strategy for computing light probe layouts efficiently under varied illumination conditions. A neural network model estimates the relative contribution of candidate probes, enabling the rapid construction of a compact configuration that maintains the scene’s indirect lighting distribution. Evaluations on complex environments indicate that the method achieves substantial speedups over conventional placement methods without compromising illumination fidelity.

BibTeX Citation

@article{tarasidis_2026
    booktitle = {Eurographics 2026 - Posters},
    editor = {Gerrits, T. and Teschner M.},
    title = {Deep Illumination–Guided Light Probe Placement}},
    author = {Tarasidis, Andreas and Vasilakis, Andreas A. and Fudos, Ioannis},
    year = {2026},
    publisher = {The Eurographics Association}
}