James F Dolan

US Patent:

20220366636, Nov 17, 2022

Filed:

May 14, 2021

Appl. No.:

17/321205

Inventors:

- Foster City CA, US
James Graham Dolan - Foster City CA, US

International Classification:

G06T 15/10
G06T 15/06
G07C 5/08

Abstract:

A sensor simulation system may generate sensor data for use in simulations by rendering two-dimensional views of a three-dimensional simulated environment. In various examples, the sensor simulation system uses sensor dependency data to determine specific views to be re-rendered at different times during the simulation. The sensor simulation system also may generate unified views with multi-sensor data at each region (e.g., pixel) of the two-dimensional view for consumption by different sensor types. A hybrid technique may be used in some implementations in which rasterization is used to generate a view, after which ray tracing is used to align the view with a particular sensor. Spatial and temporal upsampling techniques also may be used, including depth-aware and velocity-aware analyses for simulated objects, to improve view resolution and reduce the frequency of re-rendering views.

US Patent:

20220366638, Nov 17, 2022

Filed:

May 14, 2021

Appl. No.:

17/321274

Inventors:

- Foster City CA, US
James Graham Dolan - Foster City CA, US

International Classification:

G06T 15/20
G06T 7/246
G06T 7/73
G06T 17/20
G06F 30/23

Abstract:

A sensor simulation system may generate sensor data for use in simulations by rendering two-dimensional views of a three-dimensional simulated environment. In various examples, the sensor simulation system uses sensor dependency data to determine specific views to be re-rendered at different times during the simulation. The sensor simulation system also may generate unified views with multi-sensor data at each region (e.g., pixel) of the two-dimensional view for consumption by different sensor types. A hybrid technique may be used in some implementations in which rasterization is used to generate a view, after which ray tracing is used to align the view with a particular sensor. Spatial and temporal upsampling techniques also may be used, including depth-aware and velocity-aware analyses for simulated objects, to improve view resolution and reduce the frequency of re-rendering views.

US Patent:

20220366640, Nov 17, 2022

Filed:

May 14, 2021

Appl. No.:

17/321228

Inventors:

- Foster City CA, US
James Graham Dolan - Foster City CA, US

International Classification:

G06T 15/20
G06T 17/20
G06T 3/40
G06T 7/70
G06F 30/23

Abstract:

A sensor simulation system may generate sensor data for use in simulations by rendering two-dimensional views of a three-dimensional simulated environment. In various examples, the sensor simulation system uses sensor dependency data to determine specific views to be re-rendered at different times during the simulation. The sensor simulation system also may generate unified views with multi-sensor data at each region (e.g., pixel) of the two-dimensional view for consumption by different sensor types. A hybrid technique may be used in some implementations in which rasterization is used to generate a view, after which ray tracing is used to align the view with a particular sensor. Spatial and temporal upsampling techniques also may be used, including depth-aware and velocity-aware analyses for simulated objects, to improve view resolution and reduce the frequency of re-rendering views.

US Patent:

20200184027, Jun 11, 2020

Filed:

Dec 7, 2018

Appl. No.:

16/213937

Inventors:

- Foster City CA, US
James Graham Dolan - Foster City CA, US

International Classification:

G06F 17/50
G01S 17/89
G01S 13/89
G06N 20/00

Abstract:

An object model for a real-world object is constructed using a parameter-based object generator, where the parameters for the object model are determined using sensor data such as photographic images, radar images, or LIDAR images obtained from the real-world object. Conversion of the sensor data to appropriate object parameters is accomplished using a machine learning system. The machine learning system is calibrated by generating a plurality of object parameters, generating a corresponding plurality of learning objects from the plurality of object parameters, generating a plurality of simulated sensor return data for the learning objects, and then providing the simulated sensor return data and object parameters to the machine learning system.