(57) ABSTRACT A deep neural network (s)(DNN) may be used to detect objects from sensor
data of a three dimensional (3D) envi ronment. For example, a multi-view perception DNN …

A deep neural network (s)(DNN) may be used to perform panoptic segmentation by
performing pixel-level class and instance segmentation of a scene using a single pass of the …

In various examples, a deep neural network (s)(eg, a convolutional neural network) may be
trained to detect moving and stationary obstacles from RADAR data of a three dimensional …

A deep neural network (s)(DNN) may be used to detect objects from sensor data of a three
dimensional (3D) environment. For example, a multi-view perception DNN may include …

A deep neural network (s)(DNN) may be used to detect objects from sensor data of a three
dimensional (3D) environment. For example, a multi-view perception DNN may include …

In various examples, a deep neural network (s)(eg, a convolutional neural network) may be
trained to detect moving and stationary obstacles from RADAR data of a three dimensional …

VCGRFBXVSFAGGA-UHFFFAOYSA-N (1, 1-dioxo-1, 4-thiazinan-4-yl)-[6-[[3-(4-
fluorophenyl)-5-methyl-1, 2-oxazol-4-yl] methoxy] pyridin-3-yl] methanone Chemical …

A computer-implemented method comprises: generating first output using a first sensor of a
vehicle comprising an infrared camera or an event-based sensor, the first output indicating a …

2022-02-23 Assigned to Not A Satellite Labs, LLC reassignment Not A Satellite Labs, LLC
ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors …

In various examples, a deep neural network (s)(eg, a convolutional neural network) may be
trained to detect moving and stationary obstacles from RADAR data of a three dimensional …