Survey and Systematization of 3D Object Detection Models and Methods
This survey addresses the need for a structured overview in the rapidly evolving field of 3D object detection, primarily for researchers and practitioners working on autonomous vehicles and related applications, but it is incremental as it synthesizes existing work rather than introducing new methods.
The paper provides a comprehensive survey and systematization of 3D object detection models and methods from 2012-2021, covering the full pipeline from input data to detection modules to help researchers and practitioners get a quick overview of the field.
Strong demand for autonomous vehicles and the wide availability of 3D sensors are continuously fueling the proposal of novel methods for 3D object detection. In this paper, we provide a comprehensive survey of recent developments from 2012-2021 in 3D object detection covering the full pipeline from input data, over data representation and feature extraction to the actual detection modules. We introduce fundamental concepts, focus on a broad range of different approaches that have emerged over the past decade, and propose a systematization that provides a practical framework for comparing these approaches with the goal of guiding future development, evaluation and application activities. Specifically, our survey and systematization of 3D object detection models and methods can help researchers and practitioners to get a quick overview of the field by decomposing 3DOD solutions into more manageable pieces.