论文标题
基于支柱的对象检测用于自动驾驶
Pillar-based Object Detection for Autonomous Driving
论文作者
论文摘要
我们提供了一个简单而灵活的对象检测框架，该框架优化了用于自动驾驶的框架。在此应用程序中的点云非常稀疏的基础上，我们提出了一种基于支柱的实用方法来解决由锚定引起的不平衡问题。特别是，我们的算法将圆柱投影纳入多视图特征学习中，预测每个柱子的边界框参数，而不是每个锚点或每个锚点，并包括一个对齐的支柱到点投影模块，以改善最终预测。我们的无锚方法避免了与过去方法相关联的超参数搜索，从而简化了3D对象检测，同时在最新的图像上显着改善。
We present a simple and flexible object detection framework optimized for autonomous driving. Building on the observation that point clouds in this application are extremely sparse, we propose a practical pillar-based approach to fix the imbalance issue caused by anchors. In particular, our algorithm incorporates a cylindrical projection into multi-view feature learning, predicts bounding box parameters per pillar rather than per point or per anchor, and includes an aligned pillar-to-point projection module to improve the final prediction. Our anchor-free approach avoids hyperparameter search associated with past methods, simplifying 3D object detection while significantly improving upon state-of-the-art.
