Modeling configurable atmospheric conditions using point clouds for sensor simulation

A processor comprising:

• one or more circuits to:
• identify a training dataset comprising a plurality of points, each of the plurality of points associated with a corresponding label identifying a prediction for an additional point and at least one corresponding weather condition parameter;
• and
• provide the training dataset to update a neural network, the neural network to generate predictions of whether additional points are to be added for corresponding emitters of input point clouds.

A method, comprising:

• identifying, using one or more processors, a point cloud comprising a plurality of points and at least one parameter of an atmospheric condition to simulate;
• modifying, using the one or more processors, an intensity of at least one of the plurality of points according to the at least one parameter of the atmospheric condition;
• updating, using the one or more processors and based at least on a subset of the plurality of points and the at least one parameter of the atmospheric condition, a number of points comprised in the point cloud;
• determining, using the one or more processors, at least one point of the plurality of points that satisfies an emitter threshold;
• and
• replacing, using the one or more processors, the at least one point of the plurality of points with at least one additional point having a higher intensity than the at least one point.