{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:47:53Z","timestamp":1773802073362,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"14","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Recently, 3D Gaussian Splatting for scene rendering has attracted much attention in computer vision and graphics, but generally suffers from large burdens of both computation and storage when handling large-scale scenes. Some existing works in literature employ a divide-and-conquer strategy for alleviating this issue, where an input large scene is divided into lots of local blocks, and each block is handled separately. However, such a strategy generally leads to limited performance due to the inevitable inconsistency among the 3D Gaussians from different blocks. To address this problem, we propose a Consistent Anchor Guided Gaussian Splatting for large-scale scene rendering under the divide-and-conquer strategy, called CAG-GS. In CAG-GS, a set of learnable anchors for each local block is injected with the corresponding semantic features from a pre-trained semantic segmentation model SAM2 through an explored semantic mapping module, and then these anchors are used to predict the attributes of 3D Gaussians. Moreover, we explore a coarse-to-fine training strategy for CAG-GS, where each local block is optimized independently while being guided by globally consistent semantics. Extensive experimental results on five large-scale scenes demonstrate the superiority of the proposed method over five state-of-the-art methods in most cases.<\/jats:p>","DOI":"10.1609\/aaai.v40i14.38120","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:12:01Z","timestamp":1773792721000},"page":"11388-11396","source":"Crossref","is-referenced-by-count":0,"title":["CAG-GS: Consistent Anchor Guided Gaussian Splatting for Large-scale Scene Rendering"],"prefix":"10.1609","volume":"40","author":[{"given":"Shijie","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiulei","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38120\/42082","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38120\/42082","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:12:01Z","timestamp":1773792721000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38120"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i14.38120","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}