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Operator learning: Algorithms and analysis
NB Kovachki, S Lanthaler, AM Stuart - ar** between Banach spaces of functions. Such …
A survey of non‐rigid 3D registration
Non‐rigid registration computes an alignment between a source surface with a target
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …
Image matching from handcrafted to deep features: A survey
As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …
then correspond the same or similar structure/content from two or more images. Over the …
Prnet: Self-supervised learning for partial-to-partial registration
We present a simple, flexible, and general framework titled Partial Registration Network
(PRNet), for partial-to-partial point cloud registration. Inspired by recently-proposed learning …
(PRNet), for partial-to-partial point cloud registration. Inspired by recently-proposed learning …
Dynamic graph cnn for learning on point clouds
Point clouds provide a flexible geometric representation suitable for countless applications
in computer graphics; they also comprise the raw output of most 3D data acquisition devices …
in computer graphics; they also comprise the raw output of most 3D data acquisition devices …
Pde-gcn: Novel architectures for graph neural networks motivated by partial differential equations
Graph neural networks are increasingly becoming the go-to approach in various fields such
as computer vision, computational biology and chemistry, where data are naturally …
as computer vision, computational biology and chemistry, where data are naturally …
Geometric deep learning: going beyond euclidean data
Geometric deep learning is an umbrella term for emerging techniques attempting to
generalize (structured) deep neural models to non-Euclidean domains, such as graphs and …
generalize (structured) deep neural models to non-Euclidean domains, such as graphs and …
Diffusionnet: Discretization agnostic learning on surfaces
We introduce a new general-purpose approach to deep learning on three-dimensional
surfaces based on the insight that a simple diffusion layer is highly effective for spatial …
surfaces based on the insight that a simple diffusion layer is highly effective for spatial …
Splinecnn: Fast geometric deep learning with continuous b-spline kernels
Abstract We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant
of deep neural networks for irregular structured and geometric input, eg, graphs or meshes …
of deep neural networks for irregular structured and geometric input, eg, graphs or meshes …
3d-coded: 3d correspondences by deep deformation
We present a new deep learning approach for matching deformable shapes by introducing
Shape Deformation Networks which jointly encode 3D shapes and correspondences. This is …
Shape Deformation Networks which jointly encode 3D shapes and correspondences. This is …