End-to-End 3D Object Detection using LiDAR Point Cloud
This addresses object detection for autonomous vehicles, but appears incremental as it builds on existing networks and approaches.
The paper tackles 3D object detection for autonomous vehicles by proposing a novel encoding of LiDAR point clouds that avoids bird's eye view preprocessing, resulting in predictions of 3D bounding boxes and object labels.
There has been significant progress 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 used to solve this approach, we have implemented a novel model with the intention to inculcate their advantages and avoid their shortcomings. The output is predictions about the location and orientation of objects in the scene in form of 3D bounding boxes and labels of scene objects.