US 12,223,674 B2
Methods and systems for joint pose and shape estimation of objects from sensor data
Hunter Goforth, Sunnyvale, CA (US); Xiaoyan Hu, Redmond, WA (US); and Michael Happold, Pittsburgh, PA (US)
Assigned to Volkswagen Group of America Investments, LLC, Reston, VA (US)
Filed by Argo AI, LLC, Pittsburgh, PA (US)
Filed on May 17, 2023, as Appl. No. 18/318,771.
Application 18/318,771 is a continuation of application No. 16/885,743, filed on May 28, 2020, granted, now 11,694,356.
Claims priority of provisional application 62/935,958, filed on Nov. 15, 2019.
Prior Publication US 2023/0289999 A1, Sep. 14, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/00 (2017.01); B60W 30/09 (2012.01); B60W 30/095 (2012.01); B60W 60/00 (2020.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01); G06T 7/50 (2017.01); G06T 7/60 (2017.01); G06T 7/70 (2017.01); G06T 7/73 (2017.01); G06V 10/20 (2022.01); G06V 10/24 (2022.01); G06V 10/74 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G06V 20/64 (2022.01)
CPC G06T 7/73 (2017.01) [B60W 30/09 (2013.01); B60W 30/0956 (2013.01); B60W 60/0016 (2020.02); B60W 60/0027 (2020.02); G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06T 7/50 (2017.01); G06T 7/60 (2013.01); G06T 7/70 (2017.01); G06V 10/242 (2022.01); G06V 10/255 (2022.01); G06V 10/761 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G06V 20/64 (2022.01); B60W 2420/403 (2013.01); B60W 2420/408 (2024.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30244 (2013.01); G06T 2207/30261 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising, by a processor:
receiving point cloud data corresponding to an object, wherein the point cloud data is an incomplete point cloud representation of the object;
accessing a non-transitory computer readable medium storing data defining a neural network which has been trained to jointly estimate a pose and a shape of the object of a plurality of objects from incomplete point cloud data, the neural network comprising a trained shared encoder neural network, a trained pose decoder neural network, and a trained shape decoder neural network, wherein the neural network was trained by freezing or predetermining one or more parameters associated with the trained shared encoder neural network and then using corresponding codes generated by the trained shared encoder neural network to train a pose decoder neural network;
outputting, by the trained shared encoder neural network, a code representative of the point cloud data that can be used for estimating both the pose and the shape of the object;
generating, by the trained pose decoder neural network, an estimated pose of the object based on the code, the estimated pose comprising at least a heading or a translation of the object; and
generating, by the trained shape decoder neural network, an estimated shape of the object based on the code, the estimated shape comprising an estimated point cloud representation of the object that is denser than the incomplete point cloud representation of the object.