Published November 18, 2021 | Version v1
Conference paper Open

Accelerating 3D scene analysis for autonomous driving on embedded AI computing platforms

  • 1. Industrial Systems Institute, Athena Research Center, Patras Science Park, Greece
  • 2. Computer Engineering and Informatics Dept., University of Patras, Greece
  • 3. Electrical and Computer Engineering Dept., University of Patras, Greece

Description

The design of 3D object detection schemes that use point clouds as input in automotive applications has gained a lot of interest recently. Those schemes capitalize on Deep Neural Networks (DNNs) that have demonstrated impressive results in analyzing complex scenes. The proposed schemes are generally designed to improve the achieved performance, leading however to high performing approaches with high computational complexity. To mitigate this high complexity and to facilitate their deployment on edge devices, model compression and acceleration techniques can be utilized. In this paper, we propose compressed versions of two well-known 3D object detectors, namely, PointPillars and PV-RCNN, utilizing dictionary learning-based weight-sharing techniques. It is demonstrated that significant acceleration gains can be achieved with acceptable average precision loss when evaluated on the KITTI 3D object detection benchmark. These findings constitute a concrete step towards the deployment of high-performance networks in edge devices of limited resources, such as NVIDIA's Jetson TX2.

Files

Accelerating_3D_scene_analysis_for_autonomous_driving_on_embedded_AI_computing_platforms.pdf

Additional details

Funding

CPSoSaware – Cross-layer cognitive optimization tools & methods for the lifecycle support of dependable CPSoS 871738
European Commission