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Hugging Face Daily PapersKevin Qu, Tao Sun, Massimiliano Viola1 min readpaperadvanced

FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations

Read on arxiv.org

Summary

FAMOS is a feed-forward model that predicts 3D articulation, including movable-part segmentation and joint parameters, from sparse, unordered point clouds. It leverages multiple observations through a Multi-state Articulation Transformer and a novel observed articulation span objective, outperforming existing baselines.

  • FAMOS predicts movable-part segmentation and joint parameters from sparse, unordered point clouds.
  • The model jointly reasons over multiple observations, naturally supporting a variable number of inputs.
  • A Multi-state Articulation Transformer with alternating state-wise and global attention aggregates articulation cues.
  • An observed articulation span objective supervises the motion range each part exhibits across input observations.

Researchers and engineers in computer vision and robotics will find this relevant for robustly modeling articulated objects from limited sensor data, which is crucial for applications like augmented reality and robot manipulation.

7/10
machine learningcomputer visionroboticstransformers3d modelingpoint clouds

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