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As long as most of the scene remains static, probes approximate irradiance for all surfaces immersed in the irradiance volume, including novel dynamic objects. This approach, however, suffers from aliasing artifacts and high memory consumption. We propose Neural Irradiance Volume (NIV), a neural\u2010based technique that allows accurate real\u2010time rendering of diffuse global illumination via a compact pre\u2010computed model, overcoming the limitations of traditional probe\u2010based methods, such as the expensive memory footprint, aliasing artifacts, and scene\u2010specific heuristics. The key insight is that neural compression creates an adaptive and amortized representation of irradiance, circumventing the cubic scaling of grid\u2010based methods. Our superior memory\u2010scaling improves quality by at least 10x at the same memory budget, and enables a straightforward representation of higher\u2010dimensional irradiance fields, allowing rendering of time\u2010varying or dynamic effects without requiring additional computation at runtime. Unlike other neural rendering techniques, our method works within strict realtime constraints, providing fast inference (around 1 ms per frame on consumer GPUs at full HD resolution), reduced memory usage (1\u20135 MB for medium\u2010sized scenes), and only requires a G\u2010buffer as input, without expensive ray tracing or denoising.<\/jats:p>","DOI":"10.1111\/cgf.70400","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T10:10:35Z","timestamp":1776247835000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real\u2010time Rendering with a Neural Irradiance Volume"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7396-5044","authenticated-orcid":false,"given":"Arno","family":"Coomans","sequence":"first","affiliation":[{"name":"Huawei Technologies  Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3647-1659","authenticated-orcid":false,"given":"Giacomo","family":"Nazzaro","sequence":"additional","affiliation":[{"name":"Huawei Technologies  Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9101-7279","authenticated-orcid":false,"given":"Edoardo A.","family":"Dominici","sequence":"additional","affiliation":[{"name":"Huawei Technologies  Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4763-8748","authenticated-orcid":false,"given":"Christian","family":"D\u00f6ring","sequence":"additional","affiliation":[{"name":"Huawei Technologies  Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3768-0460","authenticated-orcid":false,"given":"Floor","family":"Verhoeven","sequence":"additional","affiliation":[{"name":"Huawei Technologies  Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2282-4644","authenticated-orcid":false,"given":"Konstantinos","family":"Vardis","sequence":"additional","affiliation":[{"name":"Huawei Technologies  Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5977-8536","authenticated-orcid":false,"given":"Markus","family":"Steinberger","sequence":"additional","affiliation":[{"name":"Huawei Technologies  Austria"},{"name":"Graz University of Technology  Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"e_1_2_9_2_2","unstructured":"ArvoJ. et al.: Backward ray tracing. 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