# Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
## 2 Related Work
### 3D Scene Understanding and Scene Graph  
The 3D scene understanding task requires extracting helpful information from the 3D environment, including the objects, structure and categories of entities, as well as the spatial and semantic relationships between objects. With the continuous development of point cloud deep learning, many studies on 3D scene understanding and perception have emerged, such as 3D semantic segmentation, object detection, instance segmentation and scene layout [6]. Point clouds are disordered and unstructured, and it is not feasible to use normal CNNs directly. For this reason, PointNet first considered the alignment invariance of point clouds and used a shared MLP to learn the point features and a symmetric pooling function to learn the global features, accomplishing pioneering work [4]. The later DGCNN constructs then dynamically updated the graph models in the feature space based on the point cloud space geometric features and proposed EdgeConv [5]. However, such classifications or semantic segmentation methods do not distinguish object instances. Further 3D instance segmentation tasks focus mainly on the foreground objects and ignore background elements, such as walls and floors [6]. The current point cloud-based scene understanding task still focuses on the entity object level, which cannot accurately describe the semantic relationships between objects in the scene. Further exploration is thus needed to obtain higher-level semantic information in the scenes.  
With the continuous development of the field related to 3D scene understanding, the 3D scene graph has only recently started to gain attention [11]. The concept of 3D scene graphs first appeared relying on the SLAM, which dynamically builds scene graphs based on RGBD images. Furthermore, it organizes indoor 3D scenes hierarchically, dividing indoor buildings into four layers: buildings, rooms, objects and cameras, with different layers represented by tree nodes. Although such methods are hierarchical representations in 3D scenes, it is challenging to represent the more complex semantic relationships between objects [23; 24; 25; 26]. [PERSON] et al. first proposed the scene graph prediction network (SGPN) by using the 3D scan of a scene as input data, extracting the point cloud features using PointNet, and using GCN as a relational inference network to directly predict the structure of the scene graph in the scene point cloud, thus applying it to 3D image retrieval tasks [11]. They also published the first 3D image retrieval network based on the 3 RScan dataset and released the first indoor point cloud dataset, 3 DSSG, for 3D scene graph prediction tasks [11; 12]. After that, based on this research, people began to improve their network structure to enhance its scene graph prediction effectively [13]. Recently inspired by Schemata [9], KSGPN learns a set of class-related meta-embedding vectors to learn class-related embeddings that avoid perceptual errors and effectively encode a priori labeled knowledge in a more distinguishable latent space [14].  
The 3D scene graph can also be used for specific practical tasks, such as 3D scene retrieval, scene layout, indoor object localisation, path planning and scene captioning [11; 27; 28; 29].  
Based on previous work, this paper proposes a new model to extend the 3D scene graph prediction methods and improve the classification accuracy for the point cloud-based scene graph classification tasks.