This paper develops a feed-forward model that can predict the articulation of objects from sparse, unordered point cloud observations, allowing it to learn from multiple views and generalize to new inputs. Practitioners in computer vision and robotics may care about this model as it addresses the challenge of modeling articulated objects from limited and incomplete observations.
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