Search Results for author:
Found 7 papers, 5 papers with code
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.
Revisiting Map Relations for Unsupervised Non-Rigid Shape Matching
To this end, we propose a self-adaptive functional map solver to adjust the functional map regularisation for different shape matching scenarios, together with a vertex-wise contrastive loss to obtain more discriminative features.
Fast Discrete Optimisation for Geometrically Consistent 3D Shape Matching
In this work we propose to combine the advantages of learning-based and combinatorial formalisms for 3D shape matching.
DefCor-Net: Physics-Aware Ultrasound Deformation Correction
The recovery of morphologically accurate anatomical images from deformed ones is challenging in ultrasound (US) image acquisition, but crucial to accurate and consistent diagnosis, particularly in the emerging field of computer-assisted diagnosis.
Unsupervised Learning of Robust Spectral Shape Matching
In contrast, building upon recent insights about the relation between functional maps and point-wise maps, we propose a novel unsupervised loss to couple the functional maps and point-wise maps, and thereby directly obtain point-wise maps without any post-processing.
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.
Unsupervised Deep Multi-Shape Matching
In this paper, we present a novel approach for deep multi-shape matching that ensures cycle-consistent multi-matchings while not depending on an explicit template shape.
