Multi-Modal 3D Object Detection in Autonomous Driving: a Survey
It provides a comprehensive overview for researchers in autonomous driving, but is incremental as it synthesizes existing work.
This survey reviews multi-modal 3D object detection for autonomous driving, covering sensor data, algorithms, datasets, and fusion methods, and identifies open challenges to guide future research.
In this survey, we first introduce the background of popular sensors used for self-driving, their data properties, and the corresponding object detection algorithms. Next, we discuss existing datasets that can be used for evaluating multi-modal 3D object detection algorithms. Then we present a review of multi-modal fusion based 3D detection networks, taking a close look at their fusion stage, fusion input and fusion granularity, and how these design choices evolve with time and technology. After the review, we discuss open challenges as well as possible solutions. We hope that this survey can help researchers to get familiar with the field and embark on investigations in the area of multi-modal 3D object detection.