Abstract: A method includes generating a radar-based intensity map and a lidar-based intensity map and performing one or more augmentation routines on the radar-based intensity map and the lidar-based intensity map to generate a radar input and a lidar input. The method includes generating a plurality of teacher-based bounding boxes and a plurality of student-based bounding boxes based on the radar input and the lidar input. The method includes determining a loss value of the plurality of student-based bounding boxes based on the plurality of teacher-based bounding boxes and a plurality of ground truth bounding boxes, updating one or more weights of the student neural network based on the loss value, and updating one or more weights of the teacher neural network based on a moving average associated with the one or more weights of the student neural network.

Abstract: Systems, methods, and other embodiments described herein relate improve the estimation of poses within an environment including multiple people and uncalibrated cameras. In one embodiment, a method includes acquiring sensor data including images with depth information of a surrounding environment that includes multiple people. The method includes determining 2D poses and 3D features for the people according to the sensor data. The method includes generating camera poses using at least the depth information and the features for cameras that generated the images. The method includes generating 3D poses for the people according to the camera poses and the 3D features. The method includes providing the 3D poses of the people.