Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Framework for Representing, Building and Reusing Novel State-of-the-art 3D Object Detection Models in Point Clouds Targeting Self-Driving Applications

António Linhares Silva
* ORCID logo ,
Pedro Oliveira
,
Dalila Durães
* ,
Duarte Fernandes
ORCID logo ,
Rafael Névoa
,
João Monteiro
ORCID logo ,
Pedro Melo-Pinto
ORCID logo ,
José Machado
ORCID logo ,
Paulo Novais
Version 1 : Received: 9 May 2023 / Approved: 10 May 2023 / Online: 10 May 2023 (08:21:46 CEST)

A peer-reviewed article of this Preprint also exists.

Silva, A.L.; Oliveira, P.; Durães, D.; Fernandes, D.; Névoa, R.; Monteiro, J.; Melo-Pinto, P.; Machado, J.; Novais, P. A Framework for Representing, Building and Reusing Novel State-of-the-Art Three-Dimensional Object Detection Models in Point Clouds Targeting Self-Driving Applications. Sensors 2023, 23, 6427. Silva, A.L.; Oliveira, P.; Durães, D.; Fernandes, D.; Névoa, R.; Monteiro, J.; Melo-Pinto, P.; Machado, J.; Novais, P. A Framework for Representing, Building and Reusing Novel State-of-the-Art Three-Dimensional Object Detection Models in Point Clouds Targeting Self-Driving Applications. Sensors 2023, 23, 6427.

Abstract

The rapid development of Deep Learning brought novel methodologies for 3D Object Detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, new technologies, and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it became crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements.

Keywords

Autonomous Driving; Deep learning Methods; LiDAR Sensing Technology; 3D Object Detection

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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