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With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products.
On visual similarity based 3d model retrieval
Chen, D.Y., Tian, X.P., Shen, Y.T., Ouhyoung, M.: · 2003
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Rotation invariant spherical harmonic representation of 3 d shape descriptors
Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: · 2003
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A fast learning algorithm for deep belief nets
Hinton, G.E., Osindero, S., Teh, Y.W.: · 2006
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Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: · 2008
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: · 2010
Earlier work this paper cites.
Kinectfusion: Real-time dense surface mapping and tracking
Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohi, P., Shotton, J., Hodges, S., Fitzgibbon, A.: · 2011
Earlier work this paper cites.
Probabilistic reasoning for assembly-based 3D modeling
Chaudhuri, S., Kalogerakis, E., Guibas, L., Koltun, V.: · 2011
Earlier work this paper cites.
Unsupervised learning of hierarchical representations with convolutional deep belief networks
Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: · 2011
Earlier work this paper cites.
Stacked convolutional auto-encoders for hierarchical feature extraction
Masci, J., Meier, U., Cireşan, D., Schmidhuber, J.: · 2011
Earlier work this paper cites.
A Probabilistic Model of Component-Based Shape Synthesis
Kalogerakis, E., Chaudhuri, S., Koller, D., Koltun, V.: · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., Rob, F.: · 2012
Cited alongside, same era.
Symmetry in 3d geometry: Extraction and applications
Mitra, N.J., Pauly, M., Wand, M., Ceylan, D.: · 2013
Cited alongside, same era.
Unsupervised feature learning for 3d scene labeling
Lai, K., Bo, L., Fox, D.: · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
Zeiler, M.D., Fergus, R.: · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: · 2014
Cited alongside, same era.
3d shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: · 2015
Cited alongside, same era.
Shape synthesis from sketches via procedural models and convolutional networks
Huang, H., Kalogerakis, E., Yumer, M.E., Mech, R.: · 2016
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A field model for repairing 3d shapes
Thanh Nguyen, D., Hua, B.S., Tran, K., Pham, Q.H., Yeung, S.K.: · 2016
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Unsupervised learning of 3d structure from images
Rezende, D., Eslami, S., Mohamed, S., Battaglia, P., Jaderberg, M., Heess, N.: · 2016
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Anisotropic diffusion descriptors
Boscaini, D., Masci, J., Rodolà, E., Bronstein, M.M., Cremers, D.: · 2016
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Learning shape correspondence with anisotropic convolutional neural networks
Boscaini, D., Masci, J., Rodolà, E., Bronstein, M.M.: · 2016
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3d convolutional neural networks for landing zone detection from lidar
Maturana, D., Scherer, S.: · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
Cited alongside, same era.
Learning to generate chairs with convolutional neural networks
Dosovitskiy, A., Springenberg, J., , Brox, T.: · 2015
Cited alongside, same era.
3d-r2n2: A unified approach for single and multi-view 3d object reconstruction
Choy, C.B., Xu, D., Gwak, J., Chen, K., Savarese, S.: · 2015
Cited alongside, same era.
Multi-view convolutional neural networks for 3d shape recognition
Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.G.: · 2015
Cited alongside, same era.
Wei, L., Huang, Q., Ceylan, D., Vouga, E., Li, H.: · 2016
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Deep reflectance maps
Rematas, K., Ritschel, T., Fritz, M., Gavves, E., Tuytelaars, T.: · 2016
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Delight-net: Decomposing reflectance maps into specular materials and natural illumination
Georgoulis, S., Rematas, K., Ritschel, T., Fritz, M., Gool, L., Tuytelaars, T.: · 2016
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Deep shading: Convolutional neural networks for screen-space shading
Nalbach, O., Arabadzhiyska, E., Mehta, D., Seidel, H., Ritschel, T.: · 2016
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Learning semantic deformation flows with 3d convolutional networks
Yumer, M.E., Mitra, N.J.: · 2016
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