Search Results for author:
Found 49 papers, 35 papers with code
SATR: Zero-Shot Semantic Segmentation of 3D Shapes
We explore the task of zero-shot semantic segmentation of 3D shapes by using large-scale off-the-shelf 2D image recognition models.
Understanding and Improving Features Learned in Deep Functional Maps
In this paper, we show that under some mild conditions, the features learned within deep functional map approaches can be used as point-wise descriptors and thus are directly comparable across different shapes, even without the necessity of solving for a functional map at test time.
Generalizable Local Feature Pre-training for Deformable Shape Analysis
We observe that with proper training, learned features can be useful in such tasks, but, crucially, only with an appropriate choice of the receptive field size.
NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching
We present Neural Correspondence Prior (NCP), a new paradigm for computing correspondences between 3D shapes.
TIDE: Time Derivative Diffusion for Deep Learning on Graphs
A prominent paradigm for graph neural networks is based on the message passing framework.
Equivalence Between SE(3) Equivariant Networks via Steerable Kernels and Group Convolution
Most approaches for equivariance under the Euclidean group $\mathrm{SE}(3)$ of rotations and translations fall within one of the two major categories.
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching
In this work we present a novel approach for computing correspondences between non-rigid objects, by exploiting a reduced representation of deformation fields.
Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching
Our approach is not only accurate with near-isometric input, for which a high spectral resolution is typically preferred, but also robust and able to produce reasonable matching even in the presence of significant non-isometric distortion, which poses great challenges to existing methods.
Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization
We introduce pointwise map smoothness via the Dirichlet energy into the functional map pipeline, and propose an algorithm for optimizing it efficiently, which leads to high-quality results in challenging settings.
Affection: Learning Affective Explanations for Real-World Visual Data
To embark on this journey, we introduce and share with the research community a large-scale dataset that contains emotional reactions and free-form textual explanations for 85, 007 publicly available images, analyzed by 6, 283 annotators who were asked to indicate and explain how and why they felt in a particular way when observing a specific image, producing a total of 526, 749 responses.
SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence
In this work, we present a novel learning-based framework that combines the local accuracy of contrastive learning with the global consistency of geometric approaches, for robust non-rigid matching.
Non-Isometric Shape Matching via Functional Maps on Landmark-Adapted Bases
We propose a principled approach for non-isometric landmark-preserving non-rigid shape matching.
Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching
State-of-the-art fully intrinsic networks for non-rigid shape matching often struggle to disambiguate the symmetries of the shapes leading to unstable correspondence predictions.
Implicit field supervision for robust non-rigid shape matching
Establishing a correspondence between two non-rigidly deforming shapes is one of the most fundamental problems in visual computing.
Physical Simulation Layer for Accurate 3D Modeling
We introduce a novel approach for generative 3D modeling that explicitly encourages the physical and thus functional consistency of the generated shapes.
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.
Joint Symmetry Detection and Shape Matching for Non-Rigid Point Cloud
Despite the success of deep functional maps in non-rigid 3D shape matching, there exists no learning framework that models both self-symmetry and shape matching simultaneously.
DPFM: Deep Partial Functional Maps
We consider the problem of computing dense correspondences between non-rigid shapes with potentially significant partiality.
Learning Canonical Embedding for Non-rigid Shape Matching
This paper provides a novel framework that learns canonical embeddings for non-rigid shape matching.
Differentiable Surface Triangulation
Our method builds on the result that any 2D triangulation can be achieved by a suitably perturbed weighted Delaunay triangulation.
WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration
Instead of using a predefined fixed-size local support in voxelization, we propose to learn the optimal support in a data-driven manner.
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.
Physically-Aware Generative Network for 3D Shape Modeling
In particular, we introduce a loss and a learning framework that promote two key characteristics of the generated shapes: their connectivity and physical stability.
Learning Spectral Unions of Partial Deformable 3D Shapes
Spectral geometric methods have brought revolutionary changes to the field of geometry processing.
Matrix Decomposition on Graphs: A Functional View
We propose a functional view of matrix decomposition problems on graphs such as geometric matrix completion and graph regularized dimensionality reduction.
ArtEmis: Affective Language for Visual Art
We present a novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language.
DWKS: A Local Descriptor of Deformations Between Meshes and Point Clouds
We propose a novel pointwise descriptor, called DWKS, aimed at finding correspondences across two deformable shape collections.
Learning Delaunay Surface Elements for Mesh Reconstruction
We leverage the properties of 2D Delaunay triangulations to construct a mesh from manifold surface elements.
DiffusionNet: Discretization Agnostic Learning on Surfaces
We introduce a new general-purpose approach to deep learning on 3D surfaces, based on the insight that a simple diffusion layer is highly effective for spatial communication.
Weakly Supervised Deep Functional Maps for Shape Matching
We show empirically the minimum components for obtaining state-of-the-art results with different loss functions, supervised as well as unsupervised.
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 #6 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
Geometric Matrix Completion: A Functional View
We propose a totally functional view of geometric matrix completion problem.
Weakly Supervised Deep Functional Map for Shape Matching
We show empirically minimum components for obtaining state of the art results with different loss functions, supervised as well as unsupervised.
PointTriNet: Learned Triangulation of 3D Point Sets
This work considers a new task in geometric deep learning: generating a triangulation among a set of points in 3D space.
Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation
We present a learning-based method for interpolating and manipulating 3D shapes represented as point clouds, that is explicitly designed to preserve intrinsic shape properties.
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.
Instant recovery of shape from spectrum via latent space connections
We introduce the first learning-based method for recovering shapes from Laplacian spectra.
Effective Rotation-invariant Point CNN with Spherical Harmonics kernels
We present a novel rotation invariant architecture operating directly on point cloud data.
OperatorNet: Recovering 3D Shapes From Difference Operators
This paper proposes a learning-based framework for reconstructing 3D shapes from functional operators, compactly encoded as small-sized matrices.
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
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
Point clouds obtained with 3D scanners or by image-based reconstruction techniques are often corrupted with significant amount of noise and outliers.
Unsupervised Deep Learning for Structured Shape Matching
We present a novel method for computing correspondences across 3D shapes using unsupervised learning.
Isospectralization, or how to hear shape, style, and correspondence
The question whether one can recover the shape of a geometric object from its Laplacian spectrum ('hear the shape of the drum') is a classical problem in spectral geometry with a broad range of implications and applications.
Multi-directional Geodesic Neural Networks via Equivariant Convolution
Our construction, which we call multi-directional geodesic convolution, or directional convolution for short, allows, in particular, to propagate and relate directional information across layers and thus different regions on the shape.
PCPNET: Learning Local Shape Properties from Raw Point Clouds
In this paper, we propose PCPNet, a deep-learning based approach for estimating local 3D shape properties in point clouds.
Computational Geometry
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.
Persistence-based Structural Recognition
This paper presents a framework for object recognition using topological persistence.
Unsupervised Multi-Class Joint Image Segmentation
Joint segmentation of image sets is a challenging problem, especially when there are multiple objects with variable appearance shared among the images in the collection and the set of objects present in each particular image is itself varying and unknown.
