3D Object Detection for Autonomous Driving: A Survey

Highlights：

• Notice that no recent literature exists to collect the growing knowledge concerning 3D object detection, we fill this gap by starting with several basic concepts, providing a glimpse of evolution of 3D object detection, together with comprehensive comparisons on publicly available datasets being manifested, with pros and cons being judiciously presented.

• Witnessing the absence of a universal consensus on taxonomy with respect to 3D object detection, we contribute to the maturity of the taxonomy, which keeps a good continuity of existing efforts as well as adapts new branches for dynamics.

• We present a case study on fifteen selected models among surveyed works, with regard to runtime analysis, error analysis, and robustness analysis closely. We argue that what mainly restricts the performance of detection is 3D location error based on our findings.