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Found 29 papers, 14 papers with code
Escaping Plato's Cave: Towards the Alignment of 3D and Text Latent Spaces
In this work, we investigate the possibility of a posteriori alignment of representations obtained from uni-modal 3D encoders compared to text-based feature spaces.
Localized Gaussians as Self-Attention Weights for Point Clouds Correspondence
Current data-driven methodologies for point cloud matching demand extensive training time and computational resources, presenting significant challenges for model deployment and application.
How to Blend Concepts in Diffusion Models
Our goal is to understand how operations in the latent space affect the underlying concepts.
Implicit-ARAP: Efficient Handle-Guided Deformation of High-Resolution Meshes and Neural Fields via Local Patch Meshing
Then, we apply this representation in the setting of handle-guided deformation: we introduce two distinct pipelines, which make use of 3D neural fields to compute As-Rigid-As-Possible deformations of both high-resolution meshes and neural fields under a given set of constraints.
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning
In this paper, we propose a novel framework, dubbed Detaux, whereby a weakly supervised disentanglement procedure is used to discover a new unrelated auxiliary classification task, which allows us to go from a Single-Task Learning (STL) to a Multi-Task Learning (MTL) problem.
Extracting a functional representation from a dictionary for non-rigid shape matching
Shape matching is a fundamental problem in computer graphics with many applications.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Spectral Maps for Learning on Subgraphs
We demonstrate the benefits of incorporating spectral maps in graph learning pipelines, addressing scenarios where a node-to-node map is not well defined, or in the absence of exact isomorphism.
Explanatory Learning: Beyond Empiricism in Neural Networks
We introduce Explanatory Learning (EL), a framework to let machines use existing knowledge buried in symbolic sequences -- e. g. explanations written in hieroglyphic -- by autonomously learning to interpret them.
Complex Functional Maps : a Conformal Link Between Tangent Bundles
In this paper, we introduce complex functional maps, which extend the functional map framework to conformal maps between tangent vector fields on surfaces.
Smoothness and effective regularizations in learned embeddings for shape matching
In this study, we analyze, for the first time, properties that arise in data-driven learned embedding and their relation to the shape-matching task.
Localized Shape Modelling with Global Coherence: An Inverse Spectral Approach
In this work, we address this problem by defining a data-driven model upon a family of linear operators (variants of the mesh Laplacian), whose spectra capture global and local geometric properties of the shape at hand.
Shape registration in the time of transformers
In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds.
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 spectral alignment.
Universal Spectral Adversarial Attacks for Deformable Shapes
Our attacks are universal, in that they transfer across different shapes, different representations (meshes and point clouds), and generalize to previously unseen data.
Learning Spectral Unions of Partial Deformable 3D Shapes
Spectral geometric methods have brought revolutionary changes to the field of geometry processing.
Learning disentangled representations via product manifold projection
We propose a novel approach to disentangle the generative factors of variation underlying a given set of observations.
Correspondence Learning via Linearly-invariant Embedding
However, instead of using the Laplace-Beltrami eigenfunctions as done in virtually all previous works in this domain, we demonstrate that learning the basis from data can both improve robustness and lead to better accuracy in challenging settings.
Ranked #8 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
High-Resolution Augmentation for Automatic Template-Based Matching of Human Models
This augmentation provides an effective workaround for the resolution limitations imposed by the adopted morphable model.
Infinite Feature Selection: A Graph-based Feature Filtering Approach
We propose a filtering feature selection framework that considers subsets of features as paths in a graph, where a node is a feature and an edge indicates pairwise (customizable) relations among features, dealing with relevance and redundancy principles.
Instant recovery of shape from spectrum via latent space connections
We introduce the first learning-based method for recovering shapes from Laplacian spectra.
GFrames: Gradient-Based Local Reference Frame for 3D Shape Matching
We introduce GFrames, a novel local reference frame (LRF) construction for 3D meshes and point clouds.
ZoomOut: Spectral Upsampling for Efficient Shape Correspondence
Our main observation is that high quality maps can be obtained even if the input correspondences are noisy or are encoded by a small number of coefficients in a spectral basis.
Graphics
FARM: Functional Automatic Registration Method for 3D Human Bodies
We introduce a new method for non-rigid registration of 3D human shapes.
Region-Based Correspondence Between 3D Shapes via Spatially Smooth Biclustering
This problem statement is similar to that of "biclustering", implying that RBC can be cast as a biclustering problem.
Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach
An appealing characteristic of the approach is that it aims to discover an abstraction behind low-level sensory data, that is, relevancy.
Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality
In an era where accumulating data is easy and storing it inexpensive, feature selection plays a central role in helping to reduce the high-dimensionality of huge amounts of otherwise meaningless data.
Object Tracking via Dynamic Feature Selection Processes
DFST proposes an optimized visual tracking algorithm based on the real-time selection of locally and temporally discriminative features.
Infinite Feature Selection
Filter-based feature selection has become crucial in many classification settings, especially object recognition, recently faced with feature learning strategies that originate thousands of cues.
