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

• URL: http://arxiv.org/abs/2007.00493v1
• Date: Wed, 1 Jul 2020 13:50:42 GMT
• Title: Optimisation of the PointPillars network for 3D object detection in point clouds
• Authors: Joanna Stanisz, Konrad Lis, Tomasz Kryjak, Marek Gorgon
• Abstract summary: In this paper we present our research on the optimisation of a deep neural network for 3D object detection in a point cloud. We performed the experiments for the PointPillars network, which offers a reasonable compromise between detection accuracy and calculation complexity. This will allow for real-time LiDAR data processing with low energy consumption.
• Score: 1.1470070927586016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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