Search Results for author:
Found 10 papers, 7 papers with code
Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction
We present Shape Non-rigid Kinematics (SNK), a novel zero-shot method for non-rigid shape matching that eliminates the need for extensive training or ground truth data.
Unsupervised Representation Learning for Diverse Deformable Shape Collections
Unlike previous 3D mesh autoencoders that require meshes to be in a 1-to-1 correspondence, our approach is trained on diverse meshes in an unsupervised manner.
AtomSurf : Surface Representation for Learning on Protein Structures
An essential aspect of learning from protein structures is the choice of their representation as a geometric object (be it a grid, graph, or surface), which conditions the associated learning method.
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.
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.
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.
DPFM: Deep Partial Functional Maps
We consider the problem of computing dense correspondences between non-rigid shapes with potentially significant partiality.
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.
