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Poster
3D Shape Reconstruction from Vision and Touch
Edward Smith · Roberto Calandra · Adriana Romero · Georgia Gkioxari · David Meger · Jitendra Malik · Michal Drozdzal

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #486
in Poster Session 2 »

When a toddler is presented a new toy, their instinctual behaviour is to pick it up and inspect it with their hand and eyes in tandem, clearly searching over its surface to properly understand what they are playing with. At any instance here, touch provides high fidelity localized information while vision provides complementary global context. However, in 3D shape reconstruction, the complementary fusion of visual and haptic modalities remains largely unexplored. In this paper, we study this problem and present an effective chart-based approach to multi-modal shape understanding which encourages a similar fusion vision and touch information. To do so, we introduce a dataset of simulated touch and vision signals from the interaction between a robotic hand and a large array of 3D objects. Our results show that (1) leveraging both vision and touch signals consistently improves single- modality baselines; (2) our approach outperforms alternative modality fusion methods and strongly benefits from the proposed chart-based structure; (3) the reconstruction quality increases with the number of grasps provided; and (4) the touch information not only enhances the reconstruction at the touch site but also extrapolates to its local neighborhood.

Author Information

Edward Smith (McGill University)
Roberto Calandra (Facebook AI Research)
Adriana Romero (FAIR)
Georgia Gkioxari (Facebook)
David Meger (McGill University)
Jitendra Malik (University of California at Berkley)
Michal Drozdzal (FAIR)

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