  In this work, we address the problem of 3D object detection from point cloud
data in real time. For autonomous vehicles to work, it is very important for
the perception component to detect the real world objects with both high
accuracy and fast inference. We propose a novel neural network architecture
along with the training and optimization details for detecting 3D objects in
point cloud data. We compare the results with different backbone architectures
including the standard ones like VGG, ResNet, Inception with our backbone. Also
we present the optimization and ablation studies including designing an
efficient anchor. We use the Kitti 3D Birds Eye View dataset for benchmarking
and validating our results. Our work surpasses the state of the art in this
domain both in terms of average precision and speed running at > 30 FPS. This
makes it a feasible option to be deployed in real time applications including
self driving cars.
