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We propose 3DSmoothNet, a full workflow to match 3D point clouds with a siamese deep learning architecture and fully convolutional layers using a voxelized smoothed density value (SDV) representation.
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
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S. Ioffe and C. Szegedy · 2015
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D. P. Kingma and J. L. Ba · 2015
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Voxnet: A 3d convolutional neural network for real-time object recognition
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Striving for Simplicity: The All Convolutional Net
J. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2015
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Globally consistent registration of terrestrial laser scans via graph optimization
P. Theiler, J. D. Wegner, and K. Schindler · 2015
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3D ShapeNets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
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Y. Tian, B. Fan, and F. Wu · 2017
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J. Yang, Q. Zhang, Y. Xiao, and Z. Cao · 2017
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Ppf-foldnet: Unsupervised learning of rotation invariant 3d local descriptors
H. Deng, T. Birdal, and S. Ilic · 2018
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Ppfnet: Global context aware local features for robust 3d point matching
H. Deng, T. Birdal, and S. Ilic · 2018
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Learning SO(3) Equivariant Representations with Spherical CNNs
C. Esteves, C. Allen-Blanchette, A. Makadia, and K. Daniilidis · 2018
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Learned compact local feature descriptor for tls-based geodetic monitoring of natural outdoor scenes
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Learning Local Shape Descriptors from Part Correspondences with Multiview Convolutional Networks
H. Huang, E. Kalogerakis, S. Chaudhuri, D. Ceylan, V. G. Kim, and E. Yumer · 2018
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Foldingnet: Point cloud auto-encoder via deep grid deformation
Y. Yang, C. Feng, Y. Shen, and D. Tian · 2018
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3dfeat-net: Weakly supervised local 3d features for point cloud registration
Z. J. Yew and G. H. Lee · 2018
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