Convolutional Neural Networks (CNNs) have emerged as a powerful tool for object detection in 2D images. However, their power has not been fully realised for detecting 3D objects directly in point clouds without conversion to regular grids. Moreover, existing state-of-the-art 3D object detection methods aim to recognize objects individually without exploiting their relationships during learning or inference. In this article, we first propose a strategy that associates the predictions of direction vectors with pseudo geometric centers, leading to a win-win solution for 3D bounding box candidates regression. Secondly, we propose point attention pooling to extract uniform appearance features for each 3D object proposal, benefiting from the learned direction features, semantic features and spatial coordinates of the object points. Finally, the appearance features are used together with the position features to build 3D object-object relationship graphs for all proposals to model their co-existence. We explore the effect of relation graphs on proposals’ appearance feature enhancement under supervised and unsupervised settings. The proposed relation graph network comprises a 3D object proposal generation module and a 3D relation module, making it an end-to-end trainable network for detecting 3D objects in point clouds. Experiments on challenging benchmark point cloud datasets (SunRGB-D, ScanNet and KITTI) show that our algorithm performs better than existing state-of-the-art.

中文翻译：

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点云中用于3D对象检测的关系图网络

卷积神经网络（CNN）已成为2D图像中对象检测的强大工具。但是，在不转换为规则网格的情况下直接检测点云中3D对象的功能尚未完全实现。此外，现有的最新3D对象检测方法旨在单独识别对象，而无需在学习或推理过程中利用它们之间的关系。在本文中，我们首先提出一种将方向向量的预测与伪几何中心相关联的策略，从而为3D边界框候选者回归提供双赢的解决方案。其次，我们建议利用点注意力集中技术，从学习到的方向特征，语义特征和目标点的空间坐标中提取出每个3D目标提议的统一外观特征。最后，外观特征与位置特征一起用于为所有提案建模3D对象-对象关系图以共存。我们探讨了关系图对有监督和无监督设置下提案外观特征增强的影响。所提出的关系图网络包括3D对象提议生成模块和3D关系模块，使其成为用于检测点云中3D对象的端到端可训练网络。在具有挑战性的基准点云数据集（SunRGB-D，ScanNet和KITTI）上进行的实验表明，我们的算法的性能优于现有的最新技术。我们探讨了关系图对有监督和无监督设置下提案外观特征增强的影响。所提出的关系图网络包括3D对象提议生成模块和3D关系模块，使其成为用于检测点云中3D对象的端到端可训练网络。在具有挑战性的基准点云数据集（SunRGB-D，ScanNet和KITTI）上进行的实验表明，我们的算法的性能优于现有的最新技术。我们探讨了关系图对有监督和无监督设置下提案外观特征增强的影响。所提出的关系图网络包括3D对象提议生成模块和3D关系模块，使其成为用于检测点云中3D对象的端到端可训练网络。在具有挑战性的基准点云数据集（SunRGB-D，ScanNet和KITTI）上进行的实验表明，我们的算法的性能优于现有的最新技术。