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Found 14 papers, 10 papers with code
Shape Completion in the Dark: Completing Vertebrae Morphology from 3D Ultrasound
Purpose: Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs and a lack of complete 3D information.
Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact
We also incorporate cross-attention mechanisms to capture the interplay between the objects.
Dynamic Hyperbolic Attention Network for Fine Hand-object Reconstruction
In this work, we propose the first precise hand-object reconstruction method in hyperbolic space, namely Dynamic Hyperbolic Attention Network (DHANet), which leverages intrinsic properties of hyperbolic space to learn representative features.
On the Localization of Ultrasound Image Slices within Point Distribution Models
We demonstrate that our multi-modal registration framework can localize images on the 3D surface topology of a patient-specific organ and the mean shape of an SSM.
S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences
Statistical shape models (SSMs) are an established way to represent the anatomy of a population with various clinically relevant applications.
Rotation-Invariant Transformer for Point Cloud Matching
To this end, we introduce RoITr, a Rotation-Invariant Transformer to cope with the pose variations in the point cloud matching task.
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration
More specifically, 3D structures of the whole frame are first represented by our global PPF signatures, from which structural descriptors are learned to help geometric descriptors sense the 3D world beyond local regions.
CloudAttention: Efficient Multi-Scale Attention Scheme For 3D Point Cloud Learning
The proposed hierarchical model achieves state-of-the-art shape classification in mean accuracy and yields results on par with the previous segmentation methods while requiring significantly fewer computations.
ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
Dense methods also improved pose estimation in the presence of occlusion.
Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transport
Shape matching has been a long-studied problem for the computer graphics and vision community.
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration
We study the problem of extracting correspondences between a pair of point clouds for registration.
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration
We study the problem of extracting correspondences between a pair of point clouds for registration.
Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registration
3D Point clouds are a rich source of information that enjoy growing popularity in the vision community.
Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation
Our results show that spatio-temporal information in longitudinal data is a beneficial cue for improving segmentation.
