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Existing methods often struggle with limitations such as the generation of outliers or shrinkage artifacts. Additionally, these methods usually ignore the overall spatial structure of point clouds, leading to suboptimal results. To tackle these challenges, we propose a novel framework that enhances geometric spatial consistency in upsampled point clouds through a dual\u2010supervision mechanism and enables the generation of high\u2010fidelity results with precise geometric structures. Specifically, we first design a tailored feature extractor that iteratively extracts the comprehensive and distinctive features by integrating both fine\u2010grained local geometric details and global structure information. Then, our network predicts the point\u2010to\u2010point distances and Chamfer distances of upsampled points to accurately capture the spatial relation within them. To enhance spatial consistency, we formulate a joint loss function that enables our model to perceive the spatial relations between points by indirect and direct supervision. This ensures the precise alignment between upsampled points and ground truth during training. Furthermore, we propose a coordinate reconstruction to generate more high\u2010quality upsampled points iteratively. We conduct extensive experiments across multiple benchmark datasets and downstream tasks. The results comprehensively demonstrate that our method achieves state\u2010of\u2010the\u2010art performance and exhibits superior generalisation capabilities.<\/jats:p>","DOI":"10.1049\/cit2.70052","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T18:50:34Z","timestamp":1756407034000},"page":"1291-1305","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Arbitrary\u2010Scale Point Cloud Upsampling via Enhanced Geometric Spatial Consistency"],"prefix":"10.1049","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1644-5384","authenticated-orcid":false,"given":"Xianjing","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology Harbin Institute of Technology  Shenzhen China"},{"name":"China Telecom Guizhou Branch  Guiyang China"}]},{"given":"Lintai","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Engineering Huaqiao University  Quanzhou China"}]},{"given":"Junhui","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Computer Science City University of Hong Kong  Hong Kong China"}]},{"given":"Zhijun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics Guangxi Normal University  Guilin China"}]},{"given":"Jie","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Harbin Institute of Technology  Shenzhen China"}]},{"given":"Yong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Harbin Institute of Technology  Shenzhen China"}]}],"member":"265","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"crossref","unstructured":"Y.Guo H.Wang Q.Hu H.Liu L.Liu andM.Bennamoun Deep Learning for 3D Point Clouds: A Survey(2020).","DOI":"10.1109\/TPAMI.2020.3005434"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1049\/cit2.12379"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1049\/cit2.12389"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1049\/cit2.12349"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1049\/cit2.12239"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1049\/cit2.12338"},{"key":"e_1_2_8_8_1","first-page":"9445","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wu L.","year":"2023"},{"key":"e_1_2_8_9_1","first-page":"5543","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Cai Y.","year":"2022"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/tvcg.2023.3344935"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/2945.817351"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487228.2487237"},{"key":"e_1_2_8_13_1","first-page":"2790","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Yu L.","year":"2018"},{"key":"e_1_2_8_14_1","first-page":"652","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Qi C. 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