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We introduce, TextureNet, a neural network architecture designed to extract features from high-resolution signals associated with 3D surface meshes (e.g., color texture maps).
A volumetric method for building complex models from range images
B. Curless and M. Levoy · 1996
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Geometry images
X. Gu, S. J. Gortler, and H. Hoppe · 2002
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Ptex: Per-face texture mapping for production rendering
B. Burley and D. Lacewell · 2008
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n n -symmetry direction field design
N. Ray, B. Vallet, W. C. Li, and B. Lévy · 2008
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Metric-driven RoSy field design and remeshing
Y.-K. Lai, M. Jin, X. Xie, Y. He, J. Palacios, E. Zhang, S.-M. Hu, and X. Gu · 2010
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Mnist handwritten digit database
Y. LeCun, C. Cortes, and C. Burges · 2010
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Kinectfusion: real-time 3d reconstruction and interaction using a moving depth camera
S. Izadi, D. Kim, O. Hilliges, D. Molyneaux, R. Newcombe, P. Kohli, J. Shotton, S. Hodges, D. Freeman, A. Davison, et al · 2011
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Kinectfusion: Real-time dense surface mapping and tracking
R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon · 2011
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Real-time 3d reconstruction at scale using voxel hashing
M. Nießner, M. Zollhöfer, S. Izadi, and M. Stamminger · 2013
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Shapenet: An information-rich 3d model repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, et al · 2015
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Instant field-aligned meshes
W. Jakob, M. Tarini, D. Panozzo, and O. Sorkine-Hornung · 2015
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Very high frame rate volumetric integration of depth images on mobile devices
O. Kähler, V. A. Prisacariu, C. Y. Ren, X. Sun, P. Torr, and D. Murray · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Geodesic convolutional neural networks on riemannian manifolds
J. Masci, D. Boscaini, M. Bronstein, and P. Vandergheynst · 2015
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Voxnet: A 3d convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Sun rgb-d: A rgb-d scene understanding benchmark suite
S. Song, S. P. Lichtenberg, and J. Xiao · 2015
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Multi-view convolutional neural networks for 3d shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller · 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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Learning shape correspondence with anisotropic convolutional neural networks
D. Boscaini, J. Masci, E. Rodolà, and M. Bronstein · 2016
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Anisotropic diffusion descriptors
D. Boscaini, J. Masci, E. Rodolà, M. M. Bronstein, and D. Cremers · 2016
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Volumetric and multi-view cnns for object classification on 3d data
C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. Guibas · 2016
Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
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Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. O. Ulusoy, and A. Geiger · 2017
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Semantic scene completion from a single depth image
S. Song, F. Yu, A. Zeng, A. X. Chang, M. Savva, and T. Funkhouser · 2017
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O-cnn: Octree-based convolutional neural networks for 3d shape analysis
P.-S. Wang, Y. Liu, Y.-X. Guo, C.-Y. Sun, and X. Tong · 2017
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Directionally convolutional networks for 3d shape segmentation
H. Xu, M. Dong, and Z. Zhong · 2017
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Elasticfusion: Real-time dense slam and light source estimation
T. Whelan, R. F. Salas-Moreno, B. Glocker, A. J. Davison, and S. Leutenegger · 2016
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Joint 2d-3d-semantic data for indoor scene understanding
I. Armeni, S. Sax, A. R. Zamir, and S. Savarese · 2017
Cited alongside, same era.
Matterport3d: Learning from rgb-d data in indoor environments
A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Nießner, M. Savva, S. Song, A. Zeng, and Y. Zhang · 2017
Cited alongside, same era.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
A. Dai, A. X. Chang, M. Savva, M. Halber, T. A. Funkhouser, and M. Nießner · 2017
Cited alongside, same era.
Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface reintegration
A. Dai, M. Nießner, M. Zollhöfer, S. Izadi, and C. Theobalt · 2017
Cited alongside, same era.
Shape completion using 3d-encoder-predictor cnns and shape synthesis
A. Dai, C. R. Qi, and M. Nießner · 2017
Cited alongside, same era.
Later among the works it cites.
Point convolutional neural networks by extension operators
M. Atzmon, H. Maron, and Y. Lipman · 2018
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3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation
A. Dai and M. Nießner · 2018
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Scancomplete: Large-scale scene completion and semantic segmentation for 3d scans
A. Dai, D. Ritchie, M. Bokeloh, S. Reed, J. Sturm, and M. Nießner · 2018
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A papier-mâché approach to learning 3d surface generation
T. Groueix, M. Fisher, V. G. Kim, B. C. Russell, and M. Aubry · 2018
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Quadriflow: A scalable and robust method for quadrangulation
J. Huang, Y. Zhou, M. Nießner, J. R. Shewchuk, and L. J. Guibas · 2018
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Splatnet: Sparse lattice networks for point cloud processing
H. Su, V. Jampani, D. Sun, S. Maji, E. Kalogerakis, M.-H. Yang, and J. Kautz · 2018
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Tangent convolutions for dense prediction in 3d
M. Tatarchenko, J. Park, V. Koltun, and Q.-Y. Zhou · 2018
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Feastnet: Feature-steered graph convolutions for 3d shape analysis
N. Verma, E. Boyer, and J. Verbeek · 2018
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Spherical cnns on unstructured grids
C. Jiang, J. Huang, K. Kashinath, P. Marcus, M. Niessner, et al · 2019
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