{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:47:03Z","timestamp":1773802023654,"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>3D Gaussian Splatting (3D-GS) has emerged as an efficient 3D representation and a promising foundation for semantic tasks like segmentation. However, existing 3D-GS-based segmentation methods typically rely on high-dimensional category features, which introduce substantial memory overhead. Moreover, fine-grained segmentation remains challenging due to label space congestion and the lack of stable multi-granularity control mechanisms. To address these limitations, we propose a coarse-to-fine binary encoding scheme for per-Gaussian category representation, which compresses each feature into a single integer via the binary-to-decimal mapping, drastically reducing memory usage. We further design a progressive training strategy that decomposes panoptic segmentation into a series of independent sub-tasks, reducing inter-class conflicts and thereby enhancing fine-grained segmentation capability. Additionally, we fine-tune opacity during segmentation training to address the incompatibility between photometric rendering and semantic segmentation, which often leads to foreground-background confusion. Extensive experiments on multiple benchmarks demonstrate that our method achieves state-of-the-art segmentation performance while significantly reducing memory consumption and accelerating inference.<\/jats:p>","DOI":"10.1609\/aaai.v40i14.38142","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:11:33Z","timestamp":1773792693000},"page":"11586-11594","source":"Crossref","is-referenced-by-count":0,"title":["Binary-Gaussian: Compact and Progressive Representation for 3D Gaussian Segmentation"],"prefix":"10.1609","volume":"40","author":[{"given":"An","family":"Yang","sequence":"first","affiliation":[]},{"given":"Chenyu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Du","sequence":"additional","affiliation":[]},{"given":"Jianqing","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Jia","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Jinshui","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Bing","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Cong","family":"Liu","sequence":"additional","affiliation":[]}],"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\/38142\/42104","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38142\/42104","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:11:34Z","timestamp":1773792694000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38142"}},"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.v40i14.38142","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]]}}}