Convolutional Neural Networks (CNNs) have emerged as a powerful strategy for most object detection tasks on 2D images. However, their power has not been fully realised for detecting 3D objects in point clouds directly without converting them to regular grids. Existing state-of-art 3D object detection methods aim to recognize 3D objects individually without exploiting their relationships during learning or inference. In this paper, we first propose a strategy that associates the predictions of direction vectors and pseudo geometric centers together 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 features enhancement under supervised and unsupervised settings. The proposed relation graph network consists of a 3D object proposal generation module and a 3D relation module, makes it an end-to-end trainable network for detecting 3D object in point clouds. Experiments on challenging benchmarks ( SunRGB-Dand ScanNet datasets ) of 3D point clouds show that our algorithm can perform better than the existing state-of-the-art methods.
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