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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 ofsensors like cameras, and LiDAR. Although image features are typicallypreferred, numerous approaches take spatial data as input. Exploiting thisinformation we present an approach wherein, using a novel encoding of the LiDARpoint cloud we infer the location of different classes near the autonomousvehicles. This approach does not implement a bird's eye view approach, which isgenerally applied for this application and thus saves the extensivepre-processing required. After studying the numerous networks and approachesused to solve this approach, we have implemented a novel model with theintention to inculcate their advantages and avoid their shortcomings. Theoutput is predictions about the location and orientation of objects in thescene in form of 3D bounding boxes and labels of scene objects.

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