Fetching the paper…
Reading the bibliography…
We present an adaptive deep representation of volumetric fields of 3D shapes and an efficient approach to learn this deep representation for high-quality 3D shape reconstruction and auto-encoding.
Marching Cubes: A High Resolution 3D Surface Construction Algorithm. In SIGGRAPH
William E. Lorensen and Harvey E. Cline. 1987 · 1987
Earlier work this paper cites.
A Volumetric Method for Building Complex Models from Range Images. In SIGGRAPH
Brian Curless and Marc Levoy. 1996 · 1996
Earlier work this paper cites.
Dual contouring of hermite data
Tao Ju, Frank Losasso, Scott Schaefer, and Joe Warren. 2002 · 2002
Earlier work this paper cites.
Multi-level partition of unity implicits
Yutaka Ohtake, Alexander Belyaev, Marc Alexa, Greg Turk, and Hans-Peter Seidel. 2003 · 2003
Earlier work this paper cites.
Dual marching cubes: Primal contouring of dual grids. In Pacific Graphics
Scott Schaefer and Joe Warren. 2004 · 2004
Earlier work this paper cites.
Poisson surface reconstruction. In Symp. Geom. Proc
Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe. 2006 · 2006
Earlier work this paper cites.
Interactive topology-aware surface reconstruction
Andrei Sharf, Thomas Lewiner, Gil Shklarski, Sivan Toledo, and Daniel Cohen-Or. 2007 · 2007
Earlier work this paper cites.
Volume octree with an implicitly defined dual grid
Alejandro León, Juan Carlos Torres, and Francisco Velasco. 2008 · 2008
Earlier work this paper cites.
Feature preserving point set surfaces based on non-linear kernel regression
A Cengiz Öztireli, Gael Guennebaud, and Markus Gross. 2009 · 2009
Earlier work this paper cites.
Fast Generation of Pointerless Octree Duals
Thomas Lewiner, Vinícius Mello, Adelailson Peixoto, Sinésio Pesco, and Hélio Lopes. 2010 · 2010
Earlier work this paper cites.
Data-parallel octrees for surface reconstruction
Kun Zhou, Minmin Gong, Xin Huang, and Baining Guo. 2011 · 2011
Earlier work this paper cites.
Adam: A method for stochastic optimization. In ICLR
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Signed Distance Fields for Polygon Soup Meshes. In Proceedings of Graphics Interface
Hongyi Xu and Jernej Barbič. 2014 · 2014
Earlier work this paper cites.
Fluid simulation for computer graphics
Robert Bridson. 2015 · 2015
Earlier work this paper cites.
ShapeNet: An information-rich 3D model repository
Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. 2015 · 2015
Earlier work this paper cites.
VoxNet: A 3D convolutional neural network for real-time object recognition. In IROS
Daniel Maturana and Sebastian Scherer. 2015 · 2015
Earlier work this paper cites.
U-Net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
Earlier work this paper cites.
3D ShapeNets: A deep representation for volumetric shape modeling. In CVPR
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao. 2015 · 2015
Earlier work this paper cites.
Generative and discriminative voxel modeling with convolutional neural networks. In 3D deep learning workshop (NeurIPS)
Andrew Brock, Theodore Lim, J.M. Ritchie, and Nick Weston. 2016 · 2016
Earlier work this paper cites.
3D-R2N2: A unified approach for single and multi-view 3D object reconstruction. In ECCV
Christopher Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese. 2016 · 2016
Earlier work this paper cites.
SceneNet: Understanding Real World Indoor Scenes With Synthetic Data. In CVPR
Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan, Simon Stent, and Roberto Cipolla. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition. In CVPR
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
Volumetric and multi-view CNNs for object classification on 3D data. In CVPR
Charles R. Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J. Guibas. 2016 · 2016
Earlier work this paper cites.
Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In NeurIPS
Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, and Joshua B. Tenenbaum. 2016 · 2016
Earlier work this paper cites.
A survey of surface reconstruction from point clouds
Matthew Berger, Andrea Tagliasacchi, Lee M. Seversky, Pierre Alliez, Gaël Guennebaud, Joshua A. Levine, Andrei Sharf, and Claudio T. Silva. 2017 · 2017
Earlier work this paper cites.
Dynamic FAUST: Registering human bodies in motion. In CVPR
Federica Bogo, Javier Romero, Gerard Pons-Moll, and Michael J Black. 2017 · 2017
Cited alongside, same era.
Shape completion using 3D-encoder-predictor CNNs and shape synthesis. In CVPR
Angela Dai, Charles R. Qi, and Matthias Niessner. 2017 · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry. In ICML
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
Cited alongside, same era.
Hierarchical surface prediction for 3D object reconstruction. In 3DV
Christian Häne, Shubham Tulsiani, and Jitendra Malik. 2017 · 2017
Cited alongside, same era.
Dynamic edge-conditioned filters in convolutional neural networks on graphs. In CVPR
Martin Simonovsky and Nikos Komodakis. 2017 · 2017
Cited alongside, same era.
Octree generating networks: efficient convolutional architectures for high-resolution 3D outputs. In ICCV
PointConv: Deep Convolutional Networks on 3D Point Clouds. In CVPR
Wenxuan Wu, Zhongang Qi, and Li Fuxin. 2019 · 2019
Later among the works it cites.
SAL: Sign agnostic learning of shapes from raw data. In CVPR
Matan Atzmon and Yaron Lipman. 2020 · 2020
Later among the works it cites.
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction. In ECCV
Rohan Chabra, Jan Eric Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe. 2020 · 2020
Later among the works it cites.
Implicit functions in feature space for 3d shape reconstruction and completion. In CVPR
Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll. 2020 · 2020
Later among the works it cites.
Local Deep Implicit Functions for 3D Shape. In CVPR
Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser. 2020 · 2020
Later among the works it cites.
Implicit geometric regularization for learning shapes. In ICML
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox. 2017 · 2017
Cited alongside, same era.
O-CNN: Octree-based convolutional neural networks for 3D shape analysis
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, and Xin Tong. 2017 · 2017
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
Cited alongside, same era.
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels. In CVPR
Matthias Fey, Jan Eric Lenssen, Frank Weichert, and Heinrich Müller. 2018 · 2018
Cited alongside, same era.
3D semantic segmentation with submanifold sparse convolutional networks. In CVPR
Benjamin Graham, Martin Engelcke, and Laurens van der Maaten. 2018 · 2018
Cited alongside, same era.
AtlasNet: A Papier-Mâché approach to learning 3D surface generation. In CVPR
Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, and Mathieu Aubry. 2018 · 2018
Cited alongside, same era.
PointCNN: Convolution on X-transformed points. In NeurIPS
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. 2018 · 2018
Cited alongside, same era.
Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman. 2020 · 2020
Later among the works it cites.
Local implicit grid representations for 3D scenes. In CVPR
Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, and Thomas Funkhouser. 2020 · 2020
Later among the works it cites.
NeRF: Representing scenes as neural radiance fields for view synthesis. In ECCV
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. 2020 · 2020
Later among the works it cites.
Convolutional occupancy networks. In ECCV
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. 2020 · 2020
Later among the works it cites.
Implicit Neural Representations with Periodic Activation Functions. In NeurIPS
Vincent Sitzmann, Julien NP Martel, Alexander W Bergman, David B Lindell, and Gordon Wetzstein. 2020 · 2020
Later among the works it cites.
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains. In NeurIPS
Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng. 2020 · 2020
Later among the works it cites.
Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion. In CVPR Workshop
Peng-Shuai Wang, Yang Liu, and Xin Tong. 2020 · 2020
Later among the works it cites.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020 · 2020
Later among the works it cites.
SALD: Sign Agnostic Learning with Derivatives. In ICLR
Matan Atzmon and Yaron Lipman. 2021 · 2021
Later among the works it cites.
PCT: Point cloud transformer
Meng-Hao Guo, Jun-Xiong Cai, Zheng-Ning Liu, Tai-Jiang Mu, Ralph R Martin, and Shi-Min Hu. 2021 · 2021
Later among the works it cites.
Deep Implicit Moving Least-Squares Functions for 3D Reconstruction. In CVPR
Shi-Lin Liu, Hao-Xiang Guo, Hao Pan, Peng-Shuai Wang, Xin Tong, and Yang Liu. 2021 · 2021
Later among the works it cites.
ACORN: Adaptive coordinate networks for neural scene representation
Julien N. P. Martel, David B. Lindell, Connor Z. Lin, Eric R. Chan, Marco Monteiro, and Gordon Wetzstein. 2021 · 2021
Later among the works it cites.
Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes. In CVPR
Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler. 2021 · 2021
Later among the works it cites.
OctField: Hierarchical Implicit Functions for 3D Modeling. In NeurIPS
Jia-Heng Tang, Weikai Chen, Jie Yang, Bo Wang, Songrun Liu, Bo Yang, and Lin Gao. 2021 · 2021
Later among the works it cites.
Adaptive Surface Reconstruction With Multiscale Convolutional Kernels. In ICCV
Benjamin Ummenhofer and Vladlen Koltun. 2021 · 2021
Later among the works it cites.
Neural splines: Fitting 3D surfaces with infinitely-wide neural networks. In CVPR
Francis Williams, Matthew Trager, Joan Bruna, and Denis Zorin. 2021 · 2021
Later among the works it cites.
PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds. In CVPR
Mutian Xu, Runyu Ding, Hengshuang Zhao, and Xiaojuan Qi. 2021 · 2021
Later among the works it cites.
Point transformer. In ICCV
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip Torr, and Vladlen Koltun. 2021 · 2021
Later among the works it cites.
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. 2022 · 2022
Closest in time.
Neural Fields as Learnable Kernels for 3D Reconstruction. In CVPR
Francis Williams, Zan Gojcic, Sameh Khamis, Denis Zorin, Joan Bruna, Sanja Fidler, and Or Litany. 2022 · 2022
Closest in time.