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 …
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 …
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 …
Variational autoencoders for deforming 3d mesh models
Abstract 3D geometric contents are becoming increasingly popular. In this paper, we study
the problem of analyzing deforming 3D meshes using deep neural networks. Deforming 3D …
the problem of analyzing deforming 3D meshes using deep neural networks. Deforming 3D …
Zoomout: Spectral upsampling for efficient shape correspondence
We present a simple and efficient method for refining maps or correspondences by iterative
upsampling in the spectral domain that can be implemented in a few lines of code. Our main …
upsampling in the spectral domain that can be implemented in a few lines of code. Our main …
Continuous and orientation-preserving correspondences via functional maps
We propose a method for efficiently computing orientation-preserving and approximately
continuous correspondences between non-rigid shapes, using the functional maps …
continuous correspondences between non-rigid shapes, using the functional maps …
Entropic metric alignment for correspondence problems
Many shape and image processing tools rely on computation of correspondences between
geometric domains. Efficient methods that stably extract" soft" matches in the presence of …
geometric domains. Efficient methods that stably extract" soft" matches in the presence of …
Unsupervised deep learning for structured shape matching
JM Roufosse, A Sharma… - Proceedings of the …, 2019 - openaccess.thecvf.com
We present a novel method for computing correspondences across 3D shapes using
unsupervised learning. Our method computes a non-linear transformation of given …
unsupervised learning. Our method computes a non-linear transformation of given …
Fast sinkhorn filters: Using matrix scaling for non-rigid shape correspondence with functional maps
In this paper, we provide a theoretical foundation for pointwise map recovery from functional
maps and highlight its relation to a range of shape correspondence methods based on …
maps and highlight its relation to a range of shape correspondence methods based on …