Aihua Mao;Qing Liu;Yuxuan Tang;Sheng Ye;Ran Yi;Minjing Yu;Yong-Jin Liu
{"title":"DT-Net: Point Cloud Completion Network With Neighboring Adaptive Denoiser and Splitting-Based Upsampling Transformer","authors":"Aihua Mao;Qing Liu;Yuxuan Tang;Sheng Ye;Ran Yi;Minjing Yu;Yong-Jin Liu","doi":"10.1109/TETC.2025.3573505","DOIUrl":null,"url":null,"abstract":"Point cloud completion, which involves inferring missing regions of 3D objects from partial observations, remains a challenging problem in 3D vision and robotics. Existing learning-based frameworks typically leverage an encoder-decoder architecture to predict the complete point cloud based on the global shape representation extracted from the incomplete input, or further introduce a refinement network to optimize the obtained complete point cloud in a coarse-to-fine manner, which is unable to capture fine-grained local geometric details and filled with noisy points in the thin or complex structure. In this article, we propose a novel coarse-to-fine point cloud completion framework called DT-Net, by focusing on coarse point cloud denoising and multi-level upsampling. Specifically, we propose a Neighboring Adaptive Denoiser (NAD) to effectively denoise the coarse point cloud generated by an autoencoder, and reduce noise around the slender structures, making them clear and well represented. Moreover, a novel Splitting-based Upsampling Transformer (SUT), which effectively incorporates spatial and semantic relationships between local neighborhoods in the point cloud, is also proposed for multi-level upsampling. Extensive qualitative and quantitative experiments demonstrate that our method outperforms state-of-the-art methods under widely used benchmarks.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1185-1199"},"PeriodicalIF":5.4000,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11021336/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Point cloud completion, which involves inferring missing regions of 3D objects from partial observations, remains a challenging problem in 3D vision and robotics. Existing learning-based frameworks typically leverage an encoder-decoder architecture to predict the complete point cloud based on the global shape representation extracted from the incomplete input, or further introduce a refinement network to optimize the obtained complete point cloud in a coarse-to-fine manner, which is unable to capture fine-grained local geometric details and filled with noisy points in the thin or complex structure. In this article, we propose a novel coarse-to-fine point cloud completion framework called DT-Net, by focusing on coarse point cloud denoising and multi-level upsampling. Specifically, we propose a Neighboring Adaptive Denoiser (NAD) to effectively denoise the coarse point cloud generated by an autoencoder, and reduce noise around the slender structures, making them clear and well represented. Moreover, a novel Splitting-based Upsampling Transformer (SUT), which effectively incorporates spatial and semantic relationships between local neighborhoods in the point cloud, is also proposed for multi-level upsampling. Extensive qualitative and quantitative experiments demonstrate that our method outperforms state-of-the-art methods under widely used benchmarks.
期刊介绍:
IEEE Transactions on Emerging Topics in Computing publishes papers on emerging aspects of computer science, computing technology, and computing applications not currently covered by other IEEE Computer Society Transactions. Some examples of emerging topics in computing include: IT for Green, Synthetic and organic computing structures and systems, Advanced analytics, Social/occupational computing, Location-based/client computer systems, Morphic computer design, Electronic game systems, & Health-care IT.