公开(公告)号：US20250037298A1

公开(公告)日：2025-01-30

申请号：US18910738

申请日：2024-10-09

Applicant: Aurora Operations, Inc.

Inventor： Ming Liang ,  Wei-Chiu Ma ,  Sivabalan Manivasagam ,  Raquel Urtasun ,  Bin Yang ,  Ze Yang

IPC: G06T7/70 ,  G06N20/00 ,  G06T17/20 ,  G06V40/20

Abstract: Systems and methods for generating simulation data based on real-world dynamic objects are provided. A method includes obtaining two- and three-dimensional data descriptive of a dynamic object in the real world. The two- and three-dimensional information can be provided as an input to a machine-learned model to receive object model parameters descriptive of a pose and shape modification with respect to a three-dimensional template object model. The parameters can represent a three-dimensional dynamic object model indicative of an object pose and an object shape for the dynamic object. The method can be repeated on sequential two- and three-dimensional information to generate a sequence of object model parameters over time. Portions of a sequence of parameters can be stored as simulation data descriptive of a simulated trajectory of a unique dynamic object. The parameters can be evaluated by an objective function to refine the parameters and train the machine-learned model.

公开(公告)号：US12141995B2

公开(公告)日：2024-11-12

申请号：US17388372

申请日：2021-07-29

Applicant: Aurora Operations, Inc.

Inventor： Ming Liang ,  Wei-Chiu Ma ,  Sivabalan Manivasagam ,  Raquel Urtasun ,  Bin Yang ,  Ze Yang

IPC: G06K9/00 ,  G06N20/00 ,  G06T7/70 ,  G06T17/20 ,  G06V40/20

Abstract: Systems and methods for generating simulation data based on real-world dynamic objects are provided. A method includes obtaining two- and three-dimensional data descriptive of a dynamic object in the real world. The two- and three-dimensional information can be provided as an input to a machine-learned model to receive object model parameters descriptive of a pose and shape modification with respect to a three-dimensional template object model. The parameters can represent a three-dimensional dynamic object model indicative of an object pose and an object shape for the dynamic object. The method can be repeated on sequential two- and three-dimensional information to generate a sequence of object model parameters over time. Portions of a sequence of parameters can be stored as simulation data descriptive of a simulated trajectory of a unique dynamic object. The parameters can be evaluated by an objective function to refine the parameters and train the machine-learned model.