  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 using
point cloud data. We present anchor design along with custom loss functions
used in this work. A combination of spatial and channel wise attention module
is used in this work. We use the Kitti 3D Birds Eye View dataset for
benchmarking and validating our results. Our method surpasses previous state of
the art in this domain both in terms of average precision and speed running at
> 30 FPS. Finally, we present the ablation study to demonstrate that the
performance of our network is generalizable. This makes it a feasible option to
be deployed in real time applications like self driving cars.
