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Found 16 papers, 5 papers with code
Partial-to-Partial Shape Matching with Geometric Consistency
Further, we generate a new inter-class dataset for partial-to-partial shape-matching.
SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation
In recent years, semantic segmentation has become a pivotal tool in processing and interpreting satellite imagery.
Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolation
On the one hand, by incorporating spatial maps, our method obtains more accurate and smooth point-wise correspondences compared to previous functional map methods for shape matching.
ResolvNet: A Graph Convolutional Network with multi-scale Consistency
To remedy these shortcomings, we introduce ResolvNet, a flexible graph neural network based on the mathematical concept of resolvents.
Geometrically Consistent Partial Shape Matching
Moreover, while in practice one often has only access to partial observations of a 3D shape (e. g. due to occlusion, or scanning artifacts), there do not exist any methods that directly address geometrically consistent partial shape matching.
SIGMA: Scale-Invariant Global Sparse Shape Matching
We propose a novel mixed-integer programming (MIP) formulation for generating precise sparse correspondences for highly non-rigid shapes.
G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priors
We present G-MSM (Graph-based Multi-Shape Matching), a novel unsupervised learning approach for non-rigid shape correspondence.
A Unified Framework for Implicit Sinkhorn Differentiation
The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields.
Implicit Shape Completion via Adversarial Shape Priors
Finally, we show that our adversarial training approach leads to visually plausible reconstructions that are highly consistent in recovering missing parts of a given object.
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation
These observations are paired with pixel-wise monthly semantic segmentation labels of 7 land use and land cover (LULC) classes.
Scalable Sinkhorn Backpropagation
Our main contribution is deriving a simple and efficient algorithm that performs this backward pass in closed form.
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, i. e. in a single feed forward pass, a smooth interpolation and point-to-point correspondences between them.
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport
We propose a novel unsupervised learning approach to 3D shape correspondence that builds a multiscale matching pipeline into a deep neural network.
Hamiltonian Dynamics for Real-World Shape Interpolation
While most prior work focuses on synthetic input shapes, our formulation is designed to be applicable to real-world scans with imperfect input correspondences and various types of noise.
Smooth Shells: Multi-Scale Shape Registration with Functional Maps
Smooth shells define a series of coarse-to-fine shape approximations designed to work well with multiscale algorithms.
Divergence-Free Shape Interpolation and Correspondence
We present a novel method to model and calculate deformation fields between shapes embedded in $\mathbb{R}^D$.
