{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T05:00:19Z","timestamp":1790226019133,"version":"4.1.0"},"reference-count":72,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Partial indoor point clouds acquired by light detection and ranging (LiDAR) are frequently affected by occlusion, restricted viewpoints, and non-uniform sampling, making it difficult to recover complete scene geometry while preserving the semantics of architectural structures and interior objects. To address this challenge, we propose the Structure-Aware Dual-Decoder Point Completion Network (SDD-PCN) for joint point-cloud completion and semantic segmentation. SDD-PCN first separates architectural structures from interior objects and applies dedicated multi-resolution encoders to capture their distinct geometric characteristics. The resulting local features are fused with a global scene representation and processed by parallel completion and segmentation streams that exchange information during progressive decoding. We further construct Partial-S3DIS, a partial-scene extension of the Stanford Large-Scale 3D Indoor Spaces (S3DIS) dataset, containing 1632 partial\u2013complete scene pairs generated from 272 annotated rooms through sensor-aware LiDAR simulation. Experiments show that SDD-PCN achieves L1 and squared L2 Chamfer distance (CD-L1 and CD-L2) values of 16.73 and 15.27, respectively, under the \u00d710\u22123 reporting convention. Relative to ProtoFormer, the strongest overall baseline, SDD-PCN reduces CD-L1 by 3.96% and CD-L2 by 7.90%, while obtaining the best completion performance in eight of ten scene categories. The model also achieves an overall accuracy (OA) of 87.23%, a mean class accuracy (mAcc) of 74.33%, and a mean intersection over union (mIoU) of 63.62%, outperforming both task-matched semantic scene completion baselines across all three semantic metrics. Ablation and robustness analyses confirm that structure-aware encoding and dual-stream feature interaction improve geometric recovery, particularly under moderate and severe missingness. These results demonstrate that explicitly modeling indoor structural heterogeneity provides an effective basis for unified point-based scene completion and semantic understanding.<\/jats:p>","DOI":"10.3390\/s26196005","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T15:28:43Z","timestamp":1790090923000},"page":"6005","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SDD-PCN: Structure-Aware Dual-Decoder Learning for Joint Completion and Semantic Segmentation of Partial Indoor Point Clouds"],"prefix":"10.3390","volume":"26","author":[{"given":"Xiangrong","family":"Ni","sequence":"first","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2532-6272","authenticated-orcid":false,"given":"Hanqiang","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China"},{"name":"National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China"},{"name":"National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sihang","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China"},{"name":"National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8418-7652","authenticated-orcid":false,"given":"Jian","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China"},{"name":"National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22862","DOI":"10.1109\/TITS.2022.3195555","article-title":"Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis","volume":"23","author":"Fei","year":"2022","journal-title":"IEEE Trans. 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