With the increasing demand from unmanned driving and robotics, more attention has been paid to point-cloud-based 3D object accurate detection technology. However, due to the sparseness and irregularity of the point cloud, the most critical problem is how to utilize the relevant features more efficiently. In this paper, we proposed a point-based object detection enhancement network to improve the detection accuracy in the 3D scenes understanding based on the distance features. Firstly, the distance features are extracted from the raw point sets and fused with the raw features regarding reflectivity of the point cloud to maximize the use of information in the point cloud. Secondly, we enhanced the distance features and raw features, which we collectively refer to as self-features of the key points, in set abstraction (SA) layers with the self-attention mechanism, so that the foreground points can be better distinguished from the background points. Finally, we revised the group aggregation module in SA layers to enhance the feature aggregation effect of key points. We conducted experiments on the KITTI dataset and nuScenes dataset and the results show that the enhancement method proposed in this paper has excellent performance.
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机译：随着无人驾驶和机器人技术的需求不断增长，基于点云的 3D 目标精确检测技术越来越受到关注。然而，由于点云的稀疏性和不规则性，最关键的问题是如何更有效地利用相关特征。在本文中，我们提出了一种基于点的目标检测增强网络，以提高基于距离特征的 3D 场景理解中的检测精度。首先，从原始点集中提取距离特征，并与点云反射率的原始特征融合，以最大限度地利用点云中的信息。其次，我们利用自注意力机制在集合抽象 （SA） 层中增强了距离特征和原始特征，我们将其统称为关键点的自特征，以便更好地区分前景点和背景点。最后，我们修改了 SA 层中的分组聚合模块，以增强关键点的特征聚合效果。我们在KITTI数据集和nuScenes数据集上进行了实验，结果表明，本文提出的增强方法具有优异的性能。
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