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.

本文介绍了自动驾驶所使用的流行传感器、它们的数据性质以及相应的目标检测算法。还讨论了用于评估多模态 3D 目标检测算法的现有数据集。接着对基于多模态融合的 3D 检测网络进行了回顾，并介绍了它们的融合阶段、融合输入和融合粒度以及这些设计选择如何随着时间和技术而演变。最后讨论了面临的挑战以及可能的解决方案。希望本文能帮助研究人员了解多模态 3D 目标检测领域并进行相关研究。