{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:49:21Z","timestamp":1773802161298,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"16","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>The primary goal of 3D point cloud completion is to reconstruct complete and high-resolution point clouds from incomplete and low-resolution inputs. While some recent approaches have achieved satisfactory completion performance by incorporating additional images, there remains room for improvement in fully exploiting and utilizing the rich geometric relation information contained in parts. To address this challenge, we propose a novel Semantic Guided Part Relation-aware Network (SGPRNet) for Point Cloud Completion. Its core innovation lies in establishing part semantic relations to guide the reconstruction of structurally consistent local geometries. Specifically, we utilize Large Multi-modal Models (LMMs) to automatically generate the specific text of 3D shape, which provides detailed geometric part relations descriptions. Building upon this, we design an Orthogonal Semantic Part Transfer (OSPT) module that learns transferable semantic relations between geometric parts. Subsequently, we develop a Semantic Geometric Relation-aware Transformer (SGRFormer) to progressively refine these semantic features, enhancing point cloud representation and guiding the generation of fine local structures. In addition, we establish a point-text pairs corpus, OmniObject3D-212\/34 and Text-ViPC datasets based on existing OmniObject3D and ShapeNet-ViPC datasets, incorporating the specific text. Extensive experimental results demonstrate that our method outperforms existing state-of-the-art completion methods.<\/jats:p>","DOI":"10.1609\/aaai.v40i16.38396","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:25:16Z","timestamp":1773793516000},"page":"13871-13879","source":"Crossref","is-referenced-by-count":0,"title":["Semantic Guided Part Relation-aware Network for Point Cloud Completion"],"prefix":"10.1609","volume":"40","author":[{"given":"Zhensheng","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Jianqing","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Jiye","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Zijin","family":"Du","sequence":"additional","affiliation":[]},{"given":"Chenghao","family":"Fang","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\/38396\/42358","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38396\/42358","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:25:16Z","timestamp":1773793516000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i16.38396","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]]}}}