# Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
## 1 Introduction  
Scene understanding can simulate the human cognitive process of the natural world by perceptually recognizing various contextual information in a scene [1]. From autonomous robot navigation, autonomous driving and SLAM to AR/VR and a scene's layout for interior design and architecture, research on scene understanding and perception is expanding in various fields [2, 3]. Many studies based on 3D scene understanding have emerged, such as 3D shape classification, semantic segmentation, object detection, instance segmentation and scene layout [4, 5, 6]. In practical tasks, it is no longer just satisfied with obtaining simple 3D scene semantic information, such as the object and attribute information of the scene, but it also needs to obtain richer semantic relationships between objects.  
A scene graph (SG) is a structured representation of objects and their attributes and interrelationships in a visual scene, where objects are represented as the nodes of the graph and relationships between objects are abstracted as edges [7, 8, 9]. Scene graphs based on 2D images have been applied in various scene understanding downstream tasks [10]. Due to the complexity of the object entities themselves in 3D scenes, the relevant semantic relationship features between the entities are often neglected, and there are fewer related studies on 3D scenes [11]. The scene graph structure of the semantic relationship between entities is predicted from the point cloud of the scene entities in the 3D scene graph, similar to the 2D scene graph, which abstractly represents the objects and their relationships in the 3D scene, which is due to the incompleteness of the point cloud data itself and the long-tail distribution problem of existing datasets [11, 12, 13, 14], which has a significant impact on the prediction of 3D scene graphs. Some previous point cloud-based scene graph prediction models [11, 14] directly use PointNet [4] to extract the point features and use only simplegraph neural networks (GNN) [15] to infer the relationships of entities in the scene, which has substantial limitations.  
In this paper, we fully consider the sparsity of scanned point cloud data and the long-tail distribution phenomenon of category annotation. We propose an improved 3D scene graph prediction model for efficient scene perception. It comprises three main network components: point cloud feature extraction and encoding, embedding prior knowledge learning and fusion, and the relationship inference of objects. The point cloud spatial coordinates of the entities in the real 3D-scanned scene are inputted to predict the scene semantic graph of the entity objects and their relationships. It expresses higher-level semantic relationships among the objects in the scene, which can be used to assist various scene understanding and perception tasks. The main contributions of this paper include the following:  
* A new feature extraction coding network, MP-DGCNN, extracts multiple scales of the scene entity features from point clouds more robustly.
* The ENA-GNN was introduced to perform node and edge cross-attention in message propagation, and it improves the node-edge correlation and complex relationship prediction performance.
* The long-tail distribution of the dataset is addressed under a group-weighted scheme, including category-related embedding vectors as prior knowledge and a loss function for category balance.
* The experiments are validated, showing that the proposed model achieves state-of-the-art performance.