Mitigating Point Cloud Noise in Articulated Object Manipulation

The author proposes a novel coarse-to-fine affordance learning pipeline to address the challenge of noisy point clouds in articulated object manipulation, leveraging the property that noise decreases with proximity. The approach involves two stages: learning affordance on noisy far point clouds and then refining it on less noisy local geometries.