Modeling configurable atmospheric conditions using point clouds for sensor simulation
In various examples, systems and methods are disclosed relating to transforming sensor measurements according to configurable atmospheric conditions. One or more circuits can identify a point cloud comprising a plurality of points and a parameter of a weather condition to simulate and modify an intensity of at least one of the plurality of points according to the parameter of the weather condition. The one or more circuits can update, based at least on a subset of the plurality of points and the parameter of the weather condition, the point cloud to include one or more additional points.
1 . A processor comprising:
one or more circuits to:
identify a point cloud comprising a plurality of points and at least one parameter of a weather condition to simulate;
modify an intensity of at least one of the plurality of points according to the at least one parameter of the weather condition;
update, based at least on a subset of the plurality of points and the at least one parameter of the weather condition, a number of points comprised in the point cloud;
determine at least one point of the plurality of points that satisfies an emitter threshold; and
replace 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.
2 . The processor of claim 1 , wherein the point cloud is formed according to data captured from at least one of: one or more light detection and ranging (LiDAR) sensors or one or more radio detection and ranging (RADAR) sensors.
3 . The processor of claim 1 , wherein the number of points comprised in the point cloud is updated to include one or more additional points, and the one or more circuits are to determine that each of the one or more additional points are to be included in the point cloud using a first machine learning model updated to predict, for a corresponding emitter of each point of the point cloud, whether a corresponding additional point is to be added.
4 . The processor of claim 1 , wherein the number of points comprised in the point cloud is updated to include one or more additional points, and the one or more circuits are to determine, using a second machine-learning model, a respective predicted intensity and a respective predicted distance for each of the one or more additional points.
5 . The processor of claim 1 , wherein the weather condition comprises at least one of: snow, rain, or fog, and the at least one parameter includes at least one of: snow rate, rain rate, or fog intensity, respectively.
6 . The processor of claim 1 , wherein the one or more circuits are to:
filter at least one point from the point cloud.
7 . The processor of claim 1 , wherein the one or more circuits are to modify a respective distance of a subset of the plurality of points of the point cloud.
8 . The processor of claim 1 , wherein the processor is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine;
a perception system for an autonomous or semi-autonomous machine;
a system for performing simulation operations;
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system for performing conversational AI operations;
a system for performing generative AI operations;
a system implemented using one or more large language models (LLMs);
a system implemented using one or more vision language models (VLMs);
a system for generating synthetic data;
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.
9 . 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.
10 . The processor of claim 9 , wherein the one or more circuits are to update a second neural network to generate predicted intensities for the additional points.
11 . The processor of claim 10 , wherein the one or more circuits are to update the second neural network to generate predicted distances for the additional points.
12 . The processor of claim 9 , wherein the training dataset comprises a plurality of point clouds, at least one point cloud of the training dataset being associated with at least one respective weather condition parameter and at least one respective set of labels.
13 . The processor of claim 12 , wherein the at least one respective weather condition parameter includes at least one of: snow rate, rain rate, or fog intensity, respectively.
14 . The processor of claim 9 , wherein the neural network comprises a convolutional neural network.
15 . 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.
16 . The method of claim 15 , wherein the updating the number of points comprises including one or more additional points, the method further comprising determining, using the one or more processors, that at least one of the one or more additional points are to be included in the point cloud using a first machine learning model that predicts, for a corresponding emitter of the at least one point of the point cloud, whether a corresponding additional point is to be added.
17 . The method of claim 15 , wherein the updating the number of points comprises including one or more additional points, the method further comprising determining, using the one or more processors and a second machine-learning model, a respective predicted intensity and a respective predicted distance for each of the one or more additional points.
18 . The method of claim 15 , further comprising filtering, using the one or more processors, at least one point from the point cloud.