论文标题
3DLG检测器：通过同时局部全球特征学习3D对象检测
3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning
论文作者
论文摘要
捕获不规则点云的本地和全局特征对于3D对象检测（3OD）至关重要。但是，主流3D检测器，例如，投票及其变体，在汇总操作过程中放弃了相当大的本地功能，或者忽略了整个场景中的许多全球功能。本文探讨了新的模块，以同时学习积极服务3OD的场景点云的局部全球特征。为此，我们通过同时局部全球特征学习（称为3DLG-detector）提出了一个有效的3OD网络。 3DLG检测器有两个关键贡献。首先，它开发了动态点交互（DPI）模块，该模块可在合并过程中保留有效的本地特征。此外，DPI是可拆卸的，可以将其纳入现有的3OD网络以提高其性能。其次，它开发了一个全局上下文聚合模块，以汇总编码器不同层的多尺度特征，以实现场景上下文意识。我们的方法在SUN RGB-D和扫描仪数据集的检测准确性和鲁棒性方面显示了13个竞争对手的进步。源代码将在出版物时提供。
Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD). However, mainstream 3D detectors, e.g., VoteNet and its variants, either abandon considerable local features during pooling operations or ignore many global features in the whole scene context. This paper explores new modules to simultaneously learn local-global features of scene point clouds that serve 3OD positively. To this end, we propose an effective 3OD network via simultaneous local-global feature learning (dubbed 3DLG-Detector). 3DLG-Detector has two key contributions. First, it develops a Dynamic Points Interaction (DPI) module that preserves effective local features during pooling. Besides, DPI is detachable and can be incorporated into existing 3OD networks to boost their performance. Second, it develops a Global Context Aggregation module to aggregate multi-scale features from different layers of the encoder to achieve scene context-awareness. Our method shows improvements over thirteen competitors in terms of detection accuracy and robustness on both the SUN RGB-D and ScanNet datasets. Source code will be available upon publication.
