Fetching the paper…
Reading the bibliography…
We present Neural Splines, a technique for 3D surface reconstruction that is based on random feature kernels arising from infinitely-wide shallow ReLU networks.
A fast algorithm for particle simulations
Leslie Greengard and Vladimir Rokhlin · 1987
Earlier work this paper cites.
Asymptotic formulas for the dual radon transform and applications
Donald C Solmon · 1987
Earlier work this paper cites.
Surface reconstruction from unorganized points
Hugues Hoppe, Tony DeRose, Tom Duchamp, John McDonald, and Werner Stuetzle · 1992
Earlier work this paper cites.
Reconstruction and representation of 3d objects with radial basis functions
Jonathan C Carr, Richard K Beatson, Jon B Cherrie, Tim J Mitchell, W Richard Fright, Bruce C McCallum, and Tim R Evans · 2001
Earlier work this paper cites.
Approximating and intersecting surfaces from points
Anders Adamson and Marc Alexa · 2003
Earlier work this paper cites.
On the nyström method for approximating a gram matrix for improved kernel-based learning
Petros Drineas and Michael W Mahoney · 2005
Earlier work this paper cites.
Kernel methods for implicit surface modeling
Joachim Giesen, Simon Spalinger, and Bernhard Schölkopf · 2005
Earlier work this paper cites.
Sparse surface reconstruction with adaptive partition of unity and radial basis functions
Yutaka Ohtake, Alexander Belyaev, and Hans-Peter Seidel · 2006
Earlier work this paper cites.
Fast poisson disk sampling in arbitrary dimensions
Robert Bridson · 2007
Earlier work this paper cites.
Algebraic point set surfaces
Gaël Guennebaud and Markus Gross · 2007
Earlier work this paper cites.
Continuous neural networks
Nicolas Le Roux and Yoshua Bengio · 2007
Earlier work this paper cites.
Kernel methods for deep learning
Youngmin Cho and Lawrence K Saul · 2009
Earlier work this paper cites.
A benchmark for surface reconstruction
Matthew Berger, Joshua A Levine, Luis Gustavo Nonato, Gabriel Taubin, and Claudio T Silva · 2013
Earlier work this paper cites.
Edge-aware point set resampling
Hui Huang, Shihao Wu, Minglun Gong, Daniel Cohen-Or, Uri Ascher, and Hao Zhang · 2013
Earlier work this paper cites.
Screened poisson surface reconstruction
Michael Kazhdan and Hugues Hoppe · 2013
Earlier work this paper cites.
Shapenet: An information-rich 3d model repository, 2015
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
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Breaking the curse of dimensionality with convex neural networks
Francis Bach · 2017
Cited alongside, same era.
A survey of surface reconstruction from point clouds
Matthew Berger, Andrea Tagliasacchi, Lee M Seversky, Pierre Alliez, Gael Guennebaud, Joshua A Levine, Andrei Sharf, and Claudio T Silva · 2017
Cited alongside, same era.
Neural tangent kernel: Convergence and generalization in neural networks, 2018
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
Cited alongside, same era.
Transforms and applications handbook
Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
Later among the works it cites.
How do infinite width bounded norm networks look in function space?, 2019
Pedro Savarese, Itay Evron, Daniel Soudry, and Nathan Srebro · 2019
Later among the works it cites.
Deep geometric prior for surface reconstruction
Francis Williams, Teseo Schneider, Claudio Silva, Denis Zorin, Joan Bruna, and Daniele Panozzo · 2019
Later among the works it cites.
Gradient dynamics of shallow univariate relu networks, 2019
Francis Williams, Matthew Trager, Claudio Silva, Daniele Panozzo, Denis Zorin, and Joan Bruna · 2019
Later among the works it cites.
Deep local shapes: Learning local sdf priors for detailed 3d reconstruction, 2020
Rohan Chabra, Jan Eric Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alexander D Poularikas · 2018
Cited alongside, same era.
Falkon: An optimal large scale kernel method, 2018
Alessandro Rudi, Luigi Carratino, and Lorenzo Rosasco · 2018
Cited alongside, same era.
A Continuous-Time View of Early Stopping for Least Squares
Alnur Ali, J. Zico Kolter, and Ryan J. Tibshirani · 2019
Cited alongside, same era.
Sal: Sign agnostic learning of shapes from raw data, 2019
Matan Atzmon and Yaron Lipman · 2019
Cited alongside, same era.
On the Inductive Bias of Neural Tangent Kernels
Alberto Bietti and Julien Mairal · 2019
Cited alongside, same era.
On lazy training in differentiable programming
Lenaic Chizat, Edouard Oyallon, and Francis Bach · 2019
Cited alongside, same era.
Learning elementary structures for 3d shape generation and matching, 2019
Theo Deprelle, Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, and Mathieu Aubry · 2019
Cited alongside, same era.
Benjamin Charlier, Jean Feydy, Joan Alexis Glaunès, François-David Collin, and Ghislain Durif · 2020
Closest in time.
Bsp-net: Generating compact meshes via binary space partitioning, 2020
Zhiqin Chen, Andrea Tagliasacchi, and Hao Zhang · 2020
Closest in time.
Cvxnet: Learnable convex decomposition, 2020
Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Geoffrey Hinton, and Andrea Tagliasacchi · 2020
Closest in time.
Deep manifold prior, 2020
Matheus Gadelha, Rui Wang, and Subhransu Maji · 2020
Closest in time.
Local deep implicit functions for 3d shape, 2020
Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser · 2020
Closest in time.
Implicit geometric regularization for learning shapes, 2020
Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman · 2020
Closest in time.
Point2mesh: A self-prior for deformable meshes, 2020
Rana Hanocka, Gal Metzer, Raja Giryes, and Daniel Cohen-Or · 2020
Closest in time.
Implicit neural representations with periodic activation functions, 2020
Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein · 2020
Closest in time.
Fourier features let networks learn high frequency functions in low dimensional domains, 2020
Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng · 2020
Closest in time.
Voronoinet: General functional approximators with local support
Francis Williams, Jerome Parent-Levesque, Derek Nowrouzezahrai, Daniele Panozzo, Kwang Moo Yi, and Andrea Tagliasacchi · 2020
Closest in time.