Diff-PCR uses a diffusion model to learn denoising directions for refining doubly stochastic correspondence matrices, improving point cloud registration over one-shot normalization methods.
Least-squares fitting of two 3-d point sets
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Reinforcement learning with a constellation-based reward enables direct, efficient humanoid locomotion to short-range SE(2) targets, outperforming velocity-tracking baselines in simulation and transferring to hardware.
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Diff-PCR: Diffusion-Based Correspondence Searching in Doubly Stochastic Matrix Space for Point Cloud Registration
Diff-PCR uses a diffusion model to learn denoising directions for refining doubly stochastic correspondence matrices, improving point cloud registration over one-shot normalization methods.
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No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets
Reinforcement learning with a constellation-based reward enables direct, efficient humanoid locomotion to short-range SE(2) targets, outperforming velocity-tracking baselines in simulation and transferring to hardware.