  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.
