3D Object Detection from Images for Autonomous Driving: A Survey

TL;DR

This survey comprehensively reviews recent advances in image-based 3D object detection for autonomous driving, categorizing methods, analyzing components, and discussing future challenges in this rapidly evolving field.

Contribution

It provides the first systematic organization and taxonomy of over 200 works on image-based 3D detection, filling a gap in the literature.

Findings

Summarizes common pipelines and components used in 3D detection methods.

Proposes two new taxonomies for classifying existing approaches.

Discusses current challenges and future research directions.

Abstract

3D object detection from images, one of the fundamental and challenging problems in autonomous driving, has received increasing attention from both industry and academia in recent years. Benefiting from the rapid development of deep learning technologies, image-based 3D detection has achieved remarkable progress. Particularly, more than 200 works have studied this problem from 2015 to 2021, encompassing a broad spectrum of theories, algorithms, and applications. However, to date no recent survey exists to collect and organize this knowledge. In this paper, we fill this gap in the literature and provide the first comprehensive survey of this novel and continuously growing research field, summarizing the most commonly used pipelines for image-based 3D detection and deeply analyzing each of their components. Additionally, we also propose two new taxonomies to organize the state-of-the-art methods into different categories, with the intent of providing a more systematic review of existing methods and facilitating fair comparisons with future works. In retrospect of what has been achieved so far, we also analyze the current challenges in the field and discuss future directions for image-based 3D detection research.