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 …
Recent advances in shape correspondence
Y Sahillioğlu - The Visual Computer, 2020 - Springer
Important new developments have appeared since the most recent direct survey on shape
correspondence published almost a decade ago. Our survey covers the period from 2011 …
correspondence published almost a decade ago. Our survey covers the period from 2011 …
[HTML][HTML] 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 …
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 …
Densepose: Dense human pose estimation in the wild
RA Güler, N Neverova… - Proceedings of the IEEE …, 2018 - openaccess.thecvf.com
In this work we establish dense correspondences between an RGB image and a surface-
based representation of the human body, a task we refer to as dense human pose …
based representation of the human body, a task we refer to as dense human pose …
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 …
Learning representations and generative models for 3d point clouds
P Achlioptas, O Diamanti… - … on machine learning, 2018 - proceedings.mlr.press
Three-dimensional geometric data offer an excellent domain for studying representation
learning and generative modeling. In this paper, we look at geometric data represented as …
learning and generative modeling. In this paper, we look at geometric data represented as …
Abc: A big cad model dataset for geometric deep learning
We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD)
models for research of geometric deep learning methods and applications. Each model is a …
models for research of geometric deep learning methods and applications. Each model is a …
Combining implicit function learning and parametric models for 3d human reconstruction
Implicit functions represented as deep learning approximations are powerful for
reconstructing 3D surfaces. However, they can only produce static surfaces that are not …
reconstructing 3D surfaces. However, they can only produce static surfaces that are not …
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 …