Echo-POSED learns an SO(3)×SO(3) pose representation via self-supervised equivariance to probe motion and invariance to cardiac phase from 2D slices of 3D echocardiography volumes, reporting 8.2° mean angular error in intra-patient guidance simulations.
Learning with 3D rotations, a hitchhiker’s guide to SO (3)
3 Pith papers cite this work. Polarity classification is still indexing.
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RecGen achieves state-of-the-art 3D multi-object scene reconstruction from sparse RGB-D views by combining compositional synthetic scene generation with strong 3D shape priors, outperforming SAM3D by 30%+ in shape quality and pose accuracy while using 80% fewer meshes.
ELMP performs data-efficient self-supervised adaptation of neural motion planners via analytical policy gradients and point-cloud tool encoding, raising success from 57.3% zero-shot to 89.8% in unseen environments.
citing papers explorer
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Echo-POSED: Geometric Self-Distillation for Echocardiography Guidance
Echo-POSED learns an SO(3)×SO(3) pose representation via self-supervised equivariance to probe motion and invariance to cardiac phase from 2D slices of 3D echocardiography volumes, reporting 8.2° mean angular error in intra-patient guidance simulations.
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Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations
RecGen achieves state-of-the-art 3D multi-object scene reconstruction from sparse RGB-D views by combining compositional synthetic scene generation with strong 3D shape priors, outperforming SAM3D by 30%+ in shape quality and pose accuracy while using 80% fewer meshes.
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ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients
ELMP performs data-efficient self-supervised adaptation of neural motion planners via analytical policy gradients and point-cloud tool encoding, raising success from 57.3% zero-shot to 89.8% in unseen environments.