@article{Zhao Jingjing, Lizhuang Ma_2022, title={LiDAR-based 3D Object Detection with Cylindrical Representation}, url={http://thedesignengineering.com/index.php/DE/article/view/9300}, abstractNote={<p>Cylindrical representation has shown its superiority for the LiDAR point cloud object detection task[1] for preserving sensor’s streaming property. However, little effort has been devoted to solve shape distortion and scale inconsistency problems of objects in cylindrical view, which degrades the performance. To han- dle the above limitation, we propose a novel 3D detection framework CylinDet which can benefit from the advantages of both anchor-free architecture and cylindrical data representation. First, specifically designed asymmetrical RPN and dilated asymmetric sparse backbone are employed to handle the scale inconsistency by structure-aware context embedding.&nbsp; Then, a novel cylindrical anchor-free head is proposed to encode the 3D boxes in cylindrical coordinates with range-aware supervision labels.&nbsp; Finally, our framework has been verified on the public KITTI benchmark and extensive experimental results show that our frame- work outperforms other one-stage baselines with a remarkable margin, especially on small objects (e.g., pedestrian), the performance improvement reaches nearly 8 points on average AP.<a href="#PageMark2">1</a></p&gt;}, number={1}, journal={Design Engineering}, author={Zhao Jingjing, Lizhuang Ma}, year={2022}, month={Mar.}, pages={2702 - 2713} }