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We present a deep convolutional decoder architecture that can generate volumetric 3D outputs in a compute- and memory-efficient manner by using an octree representation.
Octree encoding: A new technique for the representation, manipulation and display of arbitrary 3-d objects by computer
D. Meagher · 1980
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Linear octtrees for fast processing of three-dimensional objects
I. Gargantini · 1982
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Cumulative generation of octree models from range data
C. Connolly · 1984
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Poisson surface reconstruction
M. Kazhdan, M. Bolitho, and H. Hoppe · 2006
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SSD: Smooth Signed Distance Surface Reconstruction
F. Calakli and G. Taubin · 2011
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Fusion of depth maps with multiple scales
S. Fuhrmann and M. Goesele · 2011
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FAUST: Dataset and evaluation for 3D mesh registration
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Spatially-sparse convolutional neural networks
B. Graham · 2014
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Volumetric 3d mapping in real-time on a cpu
F. Steinbrücker, J. Sturm, and D. Cremers · 2014
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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, J. Xiao, L. Yi, and F. Yu · 2015
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Flownet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Häusser, C. Hazırbaş, V. Golkov, P. v.d. Smagt, D. Cremers, and T. Brox · 2015
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Sparse 3d convolutional neural networks
B. Graham · 2015
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Geodesic convolutional neural networks on riemannian manifolds
J. Masci, D. Boscaini, M. M. Bronstein, and P. Vandergheynst · 2015
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Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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Global, dense multiscale reconstruction for a billion points
B. Ummenhofer and T. Brox · 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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3d u-net: Learning dense volumetric segmentation from sparse annotation
Deep disentangled representations for volumetric reconstruction
E. Grant, P. Kohli, and M. van Gerven · 2016
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Unsupervised learning of 3d structure from images
D. Jimenez Rezende, S. M. A. Eslami, S. Mohamed, P. Battaglia, M. Jaderberg, and N. Heess · 2016
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Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
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Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2016
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Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. O. Ulusoy, and A. Geiger · 2016
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