Sampling is widely used in various point cloud tasks as it can effectively reduce resource consumption. Recently, some methods have proposed utilizing neural networks to optimize the sampling process for various task requirements. Currently, deep downsampling methods can be categorized into two main types: generative-based and score-based. Generative-based methods directly generate sampled point clouds using networks, whereas score-based methods assess the importance of points according to specific rules and then select sampled point clouds based on their scores. However, these methods often result in noticeable clustering effects in high-intensity feature areas, compromising their ability to preserve small-scale features and leading to the loss of some structures, thereby affecting the performance of subsequent tasks. In this paper, we propose REPS, a reconstruction-based scoring strategy that evaluates the importance of each vertex by removing and reconstructing them using surrounding vertices. Our reconstruction process comprises point reconstruction and shape reconstruction. The two aforementioned reconstruction methods effectively evaluate the importance of vertices by removing them at different scales for reconstruction. These reconstructions ensure that our method maintains the overall geometric features of the point cloud and avoids disturbing small-scale structures during sampling. Additionally, we propose the Global-Local Fusion Attention (GLFA) module, which aggregates local and global attention features of point clouds, ensuring high-quality reconstruction and sampling effects. Our method outperforms previous approaches in preserving the structural features of the sampled point clouds. Furthermore, abundant experimental results demonstrate the superior performance of our method across various common tasks.
CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；Imperial College London（伦敦帝国学院）；Royal Brompton Hospital（皇家布朗普顿医院）
Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction
Northwestern University（西北大学）；Art Institute of Chicago（芝加哥艺术学院）；Northwestern University / Art Institute of Chicago Center for Scientific Studies in the Arts (NU-ACCESS)（西北大学/芝加哥艺术学院艺术科学研究中心（NU-ACCESS））
Frequency Error-Guided Under-sampling Optimization for Multi-Contrast MRI Reconstruction
School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China（通信与信息工程学院，上海大学，上海，200444，中国）；School of Computational Mathematics Science and Engineering, Michigan State University, Ann Arbor, USA（计算数学科学与工程学院，密歇根州立大学，安阿伯，美国）；School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China（计算机科学与技术学院，华东师范大学，上海，200062，中国）
Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging
University of Chinese Academy of Sciences（中国科学院大学）；State Key Laboratory of Software Development Environment, Beihang University（北京航空航天大学软件开发环境国家重点实验室）；Peng Cheng Laboratory（鹏城实验室）
Compressed Sensing, ASBSR-method of image sampling and reconstruction and the problem of digital image acquisition with the lowest possible sampling rate
PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI
Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Peng Cheng Laboratory（鹏城实验室）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Tsinghua University（清华大学）
Active CT Reconstruction with a Learned Sampling Policy
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Pazhou Lab（琶洲实验室）；Beijing Information Science and Technology University（北京信息科技大学）；University of Science and Technology of China（中国科学技术大学）
Reconstruction of Complex-Valued Fractional Brownian Motion Fields Based on Compressive Sampling and Its Application to PSF Interpolation in Weak Lensing Survey
End-to-End Sequential Sampling and Reconstruction for MRI
University of Texas at Austin（德克萨斯大学奥斯汀分校）；California Institute of Technology（加州理工学院）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）
JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI
University of Georgia（佐治亚大学）；United Imaging Intelligence（联影智能）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能有限公司）
Learning Task-Specific Sampling Strategy for Sparse-View CT Reconstruction
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；King’s College London（伦敦国王学院）；University of Cambridge（剑桥大学）
Fast and Robust Diffusion Posterior Sampling for MR Image Reconstruction Using the Preconditioned Unadjusted Langevin Algorithm
Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria；Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, USA；Chandra Family Department of Electrical Engineering, University of Texas at Austin, USA；Department of Diagnostic Medicine, Dell Medical School, University of Texas at Austin, USA
Fine Structure-Aware Sampling: A New Sampling Training Scheme for Pixel-Aligned Implicit Models in Single-View Human Reconstruction
Nanyang Technological University（南洋理工大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；Centre for Frontier AI Research, A*STAR（新加坡科技研究局前沿人工智能研究中心）
Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling
University of Toulouse（图卢兹大学）；INP-ENSEEIHT（国立图卢兹应用科学学院ENSEEIHT校区）；ISAE-SUPAERO（法国国立高等航空航天学院）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；Laboratoire de Physique des Solides（固体物理实验室）
Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI
University of Cambridge（剑桥大学）；ShanghaiTech University（上海科技大学）；Zhejiang University of Technology（浙江工业大学）；Northwest University（西北大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）
NexOP: Joint Optimization of NEX-Aware k-space Sampling and Image Reconstruction for Low-Field MRI
Department of Electrical and Computer Engineering, Technion – Israel Institute of Technology（电气与计算机工程系，技术学院–以色列理工学院）；May-Blum-Dahl Technion Human MRI Research Center, Technion - Israel Institute of Technology（May-Blum-Dahl技术学院人类MRI研究中心，技术学院–以色列理工学院）；Department of Biomedical Engineering, Technion – Israel Institute of Technology（生物医学工程系，技术学院–以色列理工学院）
