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.
To address this and ensure consistency across tasks, we standardize the input space by adaptively scaling both the scene and object point clouds based on task- specific statistics
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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.