US 12,223,241 B2
Noise modeling using machine learning
Ashish Shrivastava, San Jose, CA (US); Surya Dwarakanath, San Francisco, CA (US); Ignacio Martin Bragado, Mountain View, CA (US); Amin Aghaei, Fremont, CA (US); and Ambrish Tyagi, Sunnyvale, CA (US)
Assigned to GM Cruise Holdings LLC, San Francisco, CA (US)
Filed by GM Cruise Holdings LLC, San Francisco, CA (US)
Filed on Jan. 3, 2023, as Appl. No. 18/092,732.
Prior Publication US 2024/0220681 A1, Jul. 4, 2024
Int. Cl. G06F 30/27 (2020.01); G06T 17/00 (2006.01)
CPC G06F 30/27 (2020.01) [G06T 17/00 (2013.01); G06T 2210/56 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus comprising:
at least one memory; and
at least one processor coupled to the at least one memory, the at least one processor configured to:
collect, using one or more virtual sensors that are operating in a simulation environment, a first set of data that represents the simulation environment, wherein the simulation environment represents virtual objects in a simulated world;
generate, using the first set of data, a point cloud that represents the simulation environment;
collect, using one or more real-world sensors, a second set of data that represents a real-world environment, wherein the real-world sensors are physical sensors;
generate, using the second set of data and a neural network, a noise model that represents noise in the second set of data that prevents the second set of data from accurately representing the real-world environment;
generate, using the noise model and the point cloud, a noisy point cloud, wherein the noisy point cloud represents or describes the real-world environment;
passing the noisy point cloud to a discriminator model that indicates whether or not the noisy point cloud was derived from a real-world environment domain; and
training the noise model, in response to the discriminator model indicating that the noisy point cloud was not derived from the real-world environment domain, wherein training the noise model includes modifying one or more weights of the noise model.