  3D scene graph generation (SGG) has been of high interest in computer vision.
Although the accuracy of 3D SGG on coarse classification and single relation
label has been gradually improved, the performance of existing works is still
far from being perfect for fine-grained and multi-label situations. In this
paper, we propose a framework fully exploring contextual information for the 3D
SGG task, which attempts to satisfy the requirements of fine-grained entity
class, multiple relation labels, and high accuracy simultaneously. Our proposed
approach is composed of a Graph Feature Extraction module and a Graph
Contextual Reasoning module, achieving appropriate information-redundancy
feature extraction, structured organization, and hierarchical inferring. Our
approach achieves superior or competitive performance over previous methods on
the 3DSSG dataset, especially on the relationship prediction sub-task.
