基于穿透点分类器的三维车辆检测中的误检去除
False Positive Removal for 3D Vehicle Detection with Penetrated Point Classifier
摘要：
最近，研究人员一直在利用LiDAR点云来提高3D车辆检测的准确性。大多数最先进的方法都是基于深度学习的，但很容易受到对象上生成的点的数量的影响。这个漏洞会导致在高召回位置出现大量的假阳性框，其中对象偶尔会被预测为很少的点。为了解决这个问题，我们引入了穿透点分类器（PPC）的基础上的激光雷达的基本属性，点不能在车辆后面生成。它确定预测框的车辆后面是否存在点，如果存在，则将该框区分为假阳性。我们在KITTI数据集上评估了我们简单而前所未有的方法，并实现了PointRCNN的性能改进，PointRCNN是最先进的方法之一。实验结果表明，在汽车类的中难度和高难度下，最高召回位置的准确率分别提高了15.46个百分点和14.63个百分点。
英文摘要：
Recently, researchers have been leveraging LiDAR point cloud for higher accuracy in 3D vehicle detection. Most state-of-the-art methods are deep learning based, but are easily affected by the number of points generated on the object. This vulnerability leads to numerous false positive boxes at high recall positions, where objects are occasionally predicted with few points. To address the issue, we introduce Penetrated Point Classifier (PPC) based on the underlying property of LiDAR that points cannot be generated behind vehicles. It determines whether a point exists behind the vehicle of the predicted box, and if does, the box is distinguished as false positive. Our straightforward yet unprecedented approach is evaluated on KITTI dataset and achieved performance improvement of PointRCNN, one of the state-of-the-art methods. The experiment results show that precision at the highest recall position is dramatically increased by 15.46 percentage points and 14.63 percentage points on the moderate and hard difficulty of car class, respectively.
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