{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:45:17Z","timestamp":1765547117073},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>Real-world sensors often produce incomplete, irregular, and noisy point clouds, making point cloud completion increasingly important. However, most existing completion methods rely on large paired datasets for training, which is labor-intensive. This paper proposes RaPD, a novel semi-supervised point cloud completion method that reduces the need for paired datasets. RaPD utilizes a two-stage training scheme, where a deep semantic prior is learned in stage 1 from unpaired complete and incomplete point clouds, and a semi-supervised prior distillation process is introduced in stage 2 to train a completion network using only a small number of paired samples. Additionally, a self-supervised completion module is introduced to improve performance using unpaired incomplete point clouds. Experiments on multiple datasets show that RaPD outperforms previous methods in both homologous and heterologous scenarios.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/81","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"726-735","source":"Crossref","is-referenced-by-count":8,"title":["Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion"],"prefix":"10.24963","author":[{"given":"Zhaoxin","family":"Fan","sequence":"first","affiliation":[{"name":"Renmin University of China"}]},{"given":"Yulin","family":"He","sequence":"additional","affiliation":[{"name":"Renmin University of China"}]},{"given":"Zhicheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Nreal"}]},{"given":"Kejian","family":"Wu","sequence":"additional","affiliation":[{"name":"Nreal"}]},{"given":"Hongyan","family":"Liu","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Jun","family":"He","sequence":"additional","affiliation":[{"name":"Renmin University of China"}]}],"member":"10584","event":{"number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2023","name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","start":{"date-parts":[[2023,8,19]]},"theme":"Artificial Intelligence","location":"Macau, SAR China","end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:34:16Z","timestamp":1691742856000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/81"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/81","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}