3D OBJECT DETECTION FOR AUTONOMOUS DRIVING USING TEMPORAL LIDAR DATA

3D object detection is a fundamental problem in the space of autonomous driving, and pedestrians are some of the most important objects to detect. The recently introduced PointPillars architecture has been shown to be effective in object detection. It voxelizes 3D LiDAR point clouds to produce a 2D pseudo-image to be used for object detection. In this work, we modify PointPillars to become a recurrent network, using fewer LiDAR frames per forward pass. Specifically, as compared to the original PointPillars model which uses 10 LiDAR frames per forward pass, our recurrent model uses 3 frames and recurrent memory. With this modification, we observe an 8% increase in pedestrian detection and a slight decline in performance on vehicle detection in a coarsely voxelized setting. Furthermore, when given 3 frames of data as input to both models, our recurrent architecture outperforms PointPillars by 21% and 1% in pedestrian and vehicle detection, respectively.