Deep Illumination–Guided Light Probe Placement
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Date
2026
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
CCS Concepts: Computing methodologies → Neural networks; Rendering
@inproceedings{10.2312:egp.20261009,
booktitle = {Eurographics 2026 - Posters},
editor = {Gerrits, Tim and Teschner, Matthias},
title = {{Deep Illumination–Guided Light Probe Placement}},
author = {Tarasidis, Andreas and Vasilakis, Andreas-Alexandros and Fudos, Ioannis },
year = {2026},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-300-1},
DOI = {10.2312/egp.20261009}
}