{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:52:24Z","timestamp":1783525944390,"version":"3.55.0"},"reference-count":86,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T00:00:00Z","timestamp":1721347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2024,7,19]]},"abstract":"<jats:p>\n            Recent techniques for real-time view synthesis have rapidly advanced in fidelity and speed, and modern methods are capable of rendering near-photorealistic scenes at interactive frame rates. At the same time, a tension has arisen between explicit scene representations amenable to rasterization and neural fields built on ray marching, with state-of-the-art instances of the latter surpassing the former in quality while being prohibitively expensive for real-time applications. We introduce SMERF, a view synthesis approach that achieves state-of-the-art accuracy among real-time methods on large scenes with footprints up to 300 m\n            <jats:sup>2<\/jats:sup>\n            at a volumetric resolution of 3.5 mm\n            <jats:sup>3<\/jats:sup>\n            . Our method is built upon two primary contributions: a hierarchical model partitioning scheme, which increases model capacity while constraining compute and memory consumption, and a distillation training strategy that simultaneously yields high fidelity and internal consistency. Our method enables full six degrees of freedom navigation in a web browser and renders in real-time on commodity smartphones and laptops. Extensive experiments show that our method exceeds the state-of-the-art in real-time novel view synthesis by 0.78 dB on standard benchmarks and 1.78 dB on large scenes, renders frames three orders of magnitude faster than state-of-the-art radiance field models, and achieves real-time performance across a wide variety of commodity devices, including smartphones. We encourage readers to explore these models interactively at our project website: https:\/\/smerf-3d.github.io.\n          <\/jats:p>","DOI":"10.1145\/3658193","type":"journal-article","created":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T14:47:57Z","timestamp":1721400477000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":47,"title":["SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8716-5934","authenticated-orcid":false,"given":"Daniel","family":"Duckworth","sequence":"first","affiliation":[{"name":"Google DeepMind, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2182-0185","authenticated-orcid":false,"given":"Peter","family":"Hedman","sequence":"additional","affiliation":[{"name":"Google Research, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1050-3958","authenticated-orcid":false,"given":"Christian","family":"Reiser","sequence":"additional","affiliation":[{"name":"Google Research, T\u00fcbingen, Germany"},{"name":"T\u00fcbingen AI Center, University of T\u00fcbingen, T\u00fcbingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8850-252X","authenticated-orcid":false,"given":"Peter","family":"Zhizhin","sequence":"additional","affiliation":[{"name":"Google Research, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8095-4121","authenticated-orcid":false,"given":"Jean-Fran\u00e7ois","family":"Thibert","sequence":"additional","affiliation":[{"name":"Google AR\/VR, Montreal, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7826-2340","authenticated-orcid":false,"given":"Mario","family":"Lu\u010di\u0107","sequence":"additional","affiliation":[{"name":"Google DeepMind, Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5300-5475","authenticated-orcid":false,"given":"Richard","family":"Szeliski","sequence":"additional","affiliation":[{"name":"Google Research, Seattle, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4016-9448","authenticated-orcid":false,"given":"Jonathan T.","family":"Barron","sequence":"additional","affiliation":[{"name":"Google Research, Alameda, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,7,19]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Neural Point-Based Graphics. 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