Rapid detection of 3D objects in indoor environments is essential for indoor mapping and modeling, robotic perception and localization, and building reconstruction. 3D point clouds acquired by a low-cost RGB-D camera have become one of the most commonly used data sources for 3D indoor mapping. However, due to the sparse surface, empty object center, and various scales of point cloud objects, 3D bounding boxes are challenging to be estimated and located accurately. To address this, geometric shape, topological structure, and object relation are commonly employed to extract box reasoning information. In this paper, we describe the geometric feature among object points as an intra-object feature and the relation feature between different objects as an inter-object feature. Based on these two features, we propose an end-to-end point cloud geometric relation network focusing on 3D object detection, which is termed as geometric relation network (GRNet). GRNet first extracts intra-object and inter-object features for each representative point using our proposed backbone network. Then, a centralization module with a scalable loss function is proposed to centralize each representative object point to its center. Next, proposal points are sampled from these shifted points, following a proposal feature pooling operation. Finally, an object-relation learning module is applied to predict bounding box parameters. Such parameters are the additive sum of prediction results from the relation-based inter-object feature and the aggregated intra-object feature. Our model achieves state-of-the-art 3D detection results with 59.1% mAP@0.25 and 39.1% mAP@0.5 on ScanNetV2 dataset, 58.4% mAP@0.25 and 34.9% mAP@0.5 on SUN RGB-D dataset.

在室内环境中快速检测3D对象对于室内制图和建模，机器人感知和定位以及建筑物重建至关重要。低成本RGB-D摄像机获取的3D点云已成为3D室内贴图的最常用数据源之一。但是，由于表面稀疏，空的对象中心和各种比例的点云对象，因此3D边界框难以准确估计和定位。为了解决这个问题，通常采用几何形状，拓扑结构和对象关系来提取框推理信息。在本文中，我们将对象点之间的几何特征描述为对象内部特征，并将不同对象之间的关系特征描述为对象间特征。基于这两个功能，我们提出了一个专注于3D对象检测的端到端点云几何关系网络，称为几何关系网络（GRNet）。GRNet首先使用我们建议的骨干网络为每个代表点提取对象内和对象间特征。然后，提出了具有可伸缩损失函数的集中模块，以将每个代表性对象点集中到其中心。接下来，在提案特征汇总操作之后，从这些移动的点中抽取提案点。最后，将对象关系学习模块应用于预测边界框参数。这些参数是来自基于关系的对象间特征和聚合的对象内特征的预测结果的累加和。我们的模型以59.1％的mAP@0.25和39.1％的mAP @ 0达到了最新的3D检测结果。