SGVF uses score-based generative models to create guiding vector fields from data distributions, enabling reliable robotic path following on complex, unordered, and branching topologies where classical methods fail.
Diffusion probabilistic models for 3d point cloud generation,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
TAX-DPD combines a feed-forward dense GMM for global placement priors with disentangled point cloud diffusion for local geometry and pose to achieve precise robotic object placement.
citing papers explorer
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Guiding Vector Field Generation via Score-based Diffusion Model
SGVF uses score-based generative models to create guiding vector fields from data distributions, enabling reliable robotic path following on complex, unordered, and branching topologies where classical methods fail.
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Disentangled Point Diffusion for Precise Object Placement
TAX-DPD combines a feed-forward dense GMM for global placement priors with disentangled point cloud diffusion for local geometry and pose to achieve precise robotic object placement.