Articulated parts can be inferred from sparse 3D views
From few 3D glimpses, a model predicts movable parts and their motion, but results depend on object shape and part size.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Kevin Qu, Tao Sun, Massimiliano Viola, Liyuan Zhu, Zhizhuo Zhou, Sayan Deb Sarkar, Konrad Schindler, Iro Armeni
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
Institution metadata: OpenAlex record ↗
What they did and found
The study trained a model to guess movable parts and their joints from a small set of partial point clouds captured from different angles. It uses a simple data generator to create more examples for training, with some errors on unfamiliar shapes and tiny parts.
Why it matters
This approach shows we can learn to spot parts you can move from limited 3D data, but results depend on shape and part size.
The method uses several incomplete views instead of a single snapshot and trains with many synthetic examples to show where parts live and how they move.
In practice, this could help flag parts from limited data, but accuracy depends on similarity to trained objects and on the size of moving parts.
Original sources · 1
- FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations ↗arXiv · 2026-09-17
Check the original paper for its authors, methods, version and access terms.
What remains uncertain
Performance drops on shapes outside training data and on very small moving parts; results come from partial views only.
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