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Physical Primitive Decomposition

  • Zhijian Liu
  • William T. Freeman
  • Joshua B. Tenenbaum
  • Jiajun WuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11216)

Abstract

Objects are made of parts, each with distinct geometry, physics, functionality, and affordances. Developing such a distributed, physical, interpretable representation of objects will facilitate intelligent agents to better explore and interact with the world. In this paper, we study physical primitive decomposition—understanding an object through its components, each with physical and geometric attributes. As annotated data for object parts and physics are rare, we propose a novel formulation that learns physical primitives by explaining both an object’s appearance and its behaviors in physical events. Our model performs well on block towers and tools in both synthetic and real scenarios; we also demonstrate that visual and physical observations often provide complementary signals. We further present ablation and behavioral studies to better understand our model and contrast it with human performance.

Notes

Acknowledgements

This work is supported by NSF #1231216, ONR MURI N00014-16-1-2007, Toyota Research Institute, and Facebook.

Supplementary material

474200_1_En_1_MOESM1_ESM.pdf (120 kb)
Supplementary material 1 (pdf 120 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zhijian Liu
    • 1
  • William T. Freeman
    • 1
    • 2
  • Joshua B. Tenenbaum
    • 1
  • Jiajun Wu
    • 1
    Email author
  1. 1.Massachusetts Institute of TechnologyCambridgeUSA
  2. 2.Google ResearchCambridgeUSA

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