  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.
