  Sensor fusion is an essential topic in many perception systems, such as
autonomous driving and robotics. Transformers-based detection head and
CNN-based feature encoder to extract features from raw sensor-data has emerged
as one of the best performing sensor-fusion 3D-detection-framework, according
to the dataset leaderboards. In this work we provide an in-depth literature
survey of transformer based 3D-object detection task in the recent past,
primarily focusing on the sensor fusion. We also briefly go through the Vision
transformers (ViT) basics, so that readers can easily follow through the paper.
Moreover, we also briefly go through few of the non-transformer based
less-dominant methods for sensor fusion for autonomous driving. In conclusion
we summarize with sensor-fusion trends to follow and provoke future research.
More updated summary can be found at:
https://github.com/ApoorvRoboticist/Transformers-Sensor-Fusion
