3D object detection based on point cloud data in the unmanned driving scene has always been a research hotspot in unmanned driving sensing technology. With the development and maturity of deep neural networks technology, the method of using neural network to detect three-dimensional object target begins to show great advantages. The experimental results show that the mismatch between anchor and training samples would affect the detection accuracy, but it has not been well solved. The contributions of this paper are as follows. For the first time, deformable convolution is introduced into the point cloud object detection network, which enhances the adaptability of the network to vehicles with different directions and shapes. Secondly, a new generation method of anchor in RPN is proposed, which can effectively prevent the mismatching between the anchor and ground truth and remove the angle classification loss in the loss function. Compared with the state-of-the-art method, the AP and AOS of the detection results are improved.

中文翻译：

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学习可变形网络用于点云上的 3D 对象检测

无人驾驶场景下基于点云数据的3D物体检测一直是无人驾驶传感技术的研究热点。随着深度神经网络技术的发展和成熟，利用神经网络检测三维物体目标的方法开始显示出巨大的优势。实验结果表明，anchor和训练样本的不匹配会影响检测精度，但一直没有得到很好的解决。本文的贡献如下。首次在点云目标检测网络中引入了可变形卷积，增强了网络对不同方向和不同形状车辆的适应性。其次，提出了RPN中anchor的新一代生成方法，可以有效防止anchor和ground truth的不匹配，去除损失函数中的角度分类损失。与最先进的方法相比，检测结果的AP和AOS都有所提高。