@inproceedings{
yao2024qebev,
title={{QE}-{BEV}: Query Evolution for Bird's Eye View Object Detection in Varied Contexts},
author={Jiawei Yao and Yingxin Lai and Hongrui Kou and Tong Wu and Ruixi Liu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=mFhwB1hmLK}
}
3D object detection plays a pivotal role in autonomous driving and robotics, demanding precise interpretation of Bird’s Eye View (BEV) images. The dynamic nature of real-world environments necessitates the use of dynamic query mechanisms in 3D object detection to adaptively capture and process the complex spatio-temporal relationships present in these scenes. However, prior implementations of dynamic queries have often faced difficulties in effectively leveraging these relationships, particularly when it comes to integrating temporal information in a computationally efficient manner. Addressing this limitation, we introduce a framework utilizing dynamic query evolution strategy, harnesses K-means clustering and Top-K attention mechanisms for refined spatio-temporal data processing.  By dynamically segmenting the BEV space and prioritizing key features through Top-K attention, our model achieves a real-time, focused analysis of pertinent scene elements. Our extensive evaluation on the nuScenes and Waymo dataset showcases a marked improvement in detection accuracy, setting a new benchmark in the domain of query-based BEV object detection. Our dynamic query evolution strategy has the potential to push the boundaries of current BEV methods with enhanced adaptability and computational efficiency. Project page: https://github.com/Jiawei-Yao0812/QE-BEV
QE-BEV: Query Evolution for Bird's Eye View Object Detection in Varied Contexts
Jiawei Yao Yingxin Lai Hongrui Kou Tong Wu Ruixi Liu