US 12,249,148 B2
Object-centric and relation-centric graph neural networks for physical property discovery
Zhenfang Chen, Cambridge, MA (US); Chuang Gan, Cambridge, MA (US); Bo Wu, Cambridge, MA (US); and Dakuo Wang, Cambridge, MA (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Mar. 24, 2022, as Appl. No. 17/656,296.
Prior Publication US 2023/0306738 A1, Sep. 28, 2023
Int. Cl. G06V 20/40 (2022.01); G06T 7/20 (2017.01); G06V 10/62 (2022.01); G06V 10/82 (2022.01); G06V 20/50 (2022.01)
CPC G06V 20/46 (2022.01) [G06T 7/20 (2013.01); G06V 10/62 (2022.01); G06V 10/82 (2022.01); G06V 20/50 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30241 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A processor-implemented method for identifying one or more intrinsic physical properties of one or more objects, the method comprising:
identifying one or more objects in a video set;
extracting observable physical properties of the identified one or more objects from the video set, including one or more static properties and one or more dynamic properties;
inferring, by a property-based graph neural network, intrinsic properties of the one or more objects based on the one or more dynamic properties;
representing the intrinsic properties of all identified objects in the video set as a graph comprising nodes and edges, wherein each node represents an identified object of the one or more identified objects and each edge represents a charge between two connected objects; and
operating an autonomous device based on the one or more inferred intrinsic properties of the one or more objects.