Survey and Systematization of 3D Object Detection Models and Methods

TL;DR

This survey comprehensively reviews 3D object detection methods from 2012 to 2021, covering data input, representations, features, and detection techniques, providing a framework for comparison and future research guidance.

Contribution

It systematically categorizes and analyzes recent 3D object detection approaches, offering a practical framework for comparison and understanding of the field.

Findings

Provides a comprehensive overview of 3D object detection methods

Introduces a systematization framework for comparing approaches

Guides future research and application in 3D detection

Abstract

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.