  In this paper we present our research on the optimisation of a deep neural
network for 3D object detection in a point cloud. Techniques like quantisation
and pruning available in the Brevitas and PyTorch tools were used. We performed
the experiments for the PointPillars network, which offers a reasonable
compromise between detection accuracy and calculation complexity. The aim of
this work was to propose a variant of the network which we will ultimately
implement in an FPGA device. This will allow for real-time LiDAR data
processing with low energy consumption. The obtained results indicate that even
a significant quantisation from 32-bit floating point to 2-bit integer in the
main part of the algorithm, results in 5%-9% decrease of the detection
accuracy, while allowing for almost a 16-fold reduction in size of the model.
