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 …
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 …
Deep geometric functional maps: Robust feature learning for shape correspondence
We present a novel learning-based approach for computing correspondences between non-
rigid 3D shapes. Unlike previous methods that either require extensive training data or …
rigid 3D shapes. Unlike previous methods that either require extensive training data or …
Spatially and spectrally consistent deep functional maps
Cycle consistency has long been exploited as a powerful prior for jointly optimizing maps
within a collection of shapes. In this paper, we investigate its utility in the approaches of …
within a collection of shapes. In this paper, we investigate its utility in the approaches of …
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 …
Learning multi-resolution functional maps with spectral attention for robust shape matching
In this work, we present a novel non-rigid shape matching framework based on multi-
resolution functional maps with spectral attention. Existing functional map learning methods …
resolution functional maps with spectral attention. Existing functional map learning methods …
Neuromorph: Unsupervised shape interpolation and correspondence in one go
We present NeuroMorph, a new neural network architecture that takes as input two 3D
shapes and produces in one go, ie in a single feed forward pass, a smooth interpolation and …
shapes and produces in one go, ie in a single feed forward pass, a smooth interpolation and …
Self-supervised learning for multimodal non-rigid 3d shape matching
The matching of 3D shapes has been extensively studied for shapes represented as surface
meshes, as well as for shapes represented as point clouds. While point clouds are a …
meshes, as well as for shapes represented as point clouds. While point clouds are a …
Unsupervised deep multi-shape matching
Abstract 3D shape matching is a long-standing problem in computer vision and computer
graphics. While deep neural networks were shown to lead to state-of-the-art results in shape …
graphics. While deep neural networks were shown to lead to state-of-the-art results in shape …
Unsupervised deep learning for structured shape matching
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 …