End-to-End 3D Object Detection using LiDAR Point Cloud

Significant progress has been made in the field of autonomous vehicles. Object detection and tracking are the primary tasks for any autonomous vehicle. The task of object detection in autonomous vehicles relies on a variety of sensors like cameras, and LiDAR. Although image features are typically preferred, numerous approaches take spatial data as input. Exploiting this information we present an approach wherein, using a novel encoding of the LiDAR point cloud we infer the location of different classes near the autonomous vehicles. This approach does not implement a bird's eye view approach, which is generally applied for this application and thus saves the extensive pre-processing required. After studying the numerous networks and approaches, we have implemented a novel model intending to inculcate their advantages and avoid their shortcomings. The output is predictions about the location and orientation of objects in the scene in the form of 3D bounding boxes and labels of scene objects.