  Autonomous driving is regarded as one of the most promising remedies to
shield human beings from severe crashes. To this end, 3D object detection
serves as the core basis of perception stack especially for the sake of path
planning, motion prediction, and collision avoidance etc. Taking a quick glance
at the progress we have made, we attribute challenges to visual appearance
recovery in the absence of depth information from images, representation
learning from partially occluded unstructured point clouds, and semantic
alignments over heterogeneous features from cross modalities. Despite existing
efforts, 3D object detection for autonomous driving is still in its infancy.
Recently, a large body of literature have been investigated to address this 3D
vision task. Nevertheless, few investigations have looked into collecting and
structuring this growing knowledge. We therefore aim to fill this gap in a
comprehensive survey, encompassing all the main concerns including sensors,
datasets, performance metrics and the recent state-of-the-art detection
methods, together with their pros and cons. Furthermore, we provide
quantitative comparisons with the state of the art. A case study on fifteen
selected representative methods is presented, involved with runtime analysis,
error analysis, and robustness analysis. Finally, we provide concluding remarks
after an in-depth analysis of the surveyed works and identify promising
directions for future work.
