Results 71 to 80 of about 309,652 (283)
Learning Polynomial-Based Separable Convolution for 3D Point Cloud Analysis
Sensors, 2021 Shape classification and segmentation of point cloud data are two of the most demanding tasks in photogrammetry and remote sensing applications, which aim to recognize object categories or point labels.
Ruixuan Yu, Jian Sun
doaj +1 more source
3D Point Capsule Networks [PDF]
, 2018 In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
Birdal, Tolga +3 more
core
3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration
, 2018 In this paper, we propose the 3DFeat-Net which learns both 3D feature detector and descriptor for point cloud matching using weak supervision. Unlike many existing works, we do not require manual annotation of matching point clusters.
A Gordo +12 more
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Bio‐Inspired Molecular Events in Poly(Ionic Liquids)
Advanced Functional Materials, EarlyView.Originating from dipolar and polar inter‐ and intra‐chain interactions of the building blocks, the topologies and morphologies of poly(ionic liquids) (PIL) govern their nano‐ and micro‐processibility. Modulating the interactions of cation‐anion pairs with aliphatic dipolar components enables the tunability of properties, facilitated by “bottom‐up ...
Jiahui Liu, Marek W. Urban
wiley +1 more source
Learned 3D Shape Descriptors for Classifying 3D Point Cloud Models [PDF]
Proceedings of CAD'16, 2016 ABSTRACTRecent hardware advances in the field of 3D imaging are democratizing 3D sensing with tremendous scientific and societal implications. Point cloud processing has traditionally required meshing the point clouds output by 3D cameras followed by surface reconstruction, and geometric feature recognition.
Xiaojun Zhao, Horea T. Ilieş
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Shape-invariant 3D Adversarial Point Clouds
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Adversary and invisibility are two fundamental but conflict characters of adversarial perturbations. Previous adversarial attacks on 3D point cloud recognition have often been criticized for their noticeable point outliers, since they just involve an "implicit constrain" like global distance loss in the time-consuming optimization to limit the ...
Huang, Qidong +5 more
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Advanced Functional Materials, EarlyView.Automat optical inspection (AOI) techniques in semiconductor fabrication can be leveraged in battery manufacturing, enabling scalable detection and analysis of electrode‐ and cell‐level imperfections through AI‐driven analytics and a digital‐twin framework.
Jianyu Li, Ertao Hu, Wei Wei, Feifei Shi
wiley +1 more source
3D-PCGR: Colored Point Cloud Generation and Reconstruction with Surface and Scale Constraints
Remote SensingIn the field of 3D point cloud data, the 3D representation of objects is often affected by factors such as lighting, occlusion, and noise, leading to issues of information loss and incompleteness in the collected point cloud data.
Chaofeng Yuan +4 more
doaj +1 more source
3D Point Cloud Classification with ACGAN-3D and VACWGAN-GP
Turkish Journal of Electrical Engineering and Computer Sciences, 2023 Machine learning and deep learning techniques are widely used to make sense of 3D point cloud data which became ubiquitous and important due to the recent advances in 3D scanning technologies and other sensors. In this work, we propose two networks to predict the class of the input 3D point cloud: 3D Auxiliary Classifier Generative Adversarial Network (
Ergün, Onur, Sahillioglu, Yusuf
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Advanced Functional Materials, EarlyView.A Complexation‐Mediated Diffusion‐Limited Growth (CMDLG) framework is established to rationalize the anisotropic growth of lead‐free perovskites. Integrating coordination chemistry with mass transport kinetics, this study theoretically derives and experimentally validates that stable iodocuprate complexes induce a diffusion‐limited regime.
Hyunmin Lee +5 more
wiley +1 more source