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Found 9 papers, 5 papers with code
Rotation-Invariant Transformer for Point Cloud Matching
The intrinsic rotation invariance lies at the core of matching point clouds with handcrafted descriptors, but it is despised by most of the recent deep matchers.
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.
