# Optimisation of the PointPillars network for 3D object detection in point clouds

Source: [Optimisation of the PointPillars network for 3D object detection in point clouds](<https://www.sota2.com/research/paper/68fa699e1e209131cea2321b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:04:46.235Z

Authors: Joanna Stanisz, Konrad Lis, Tomasz Kryjak, Marek Gorgon

Published: 2020-07-01T13:50:42.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.00493>)

[PDF](<https://arxiv.org/pdf/2007.00493.pdf>)