Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimisation of the PointPillars network for 3D object detection in
  point clouds   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.

29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics