# Semantic Component Association within Object Classes Based on Convex Polyhedrons
## 2 Related Research  
Semantic segmentation in 3D was recently established as an important task in various applications: autonomous driving, human-machine interaction, object manipulation, manufacturing, geometric modeling, and reconstruction of 3D scenes, just to name a few. To facilitate the development of 3D shape understanding, the ShapeNet challenge [4] for semantic 3D shape segmentation on a large-scale 3D shape database was proposed. In this challenge, ShapeNet Parts, a subset of 16 classes from the ShapeNet database [5], was used, which is also used in the experimental analysis reported in this paper. The PointCNN method introduced in the ShapeNet challenge was later improved in [6]. It represents a generalization of the typical convolutional neural network (CNN) architecture for feature learning from point clouds. The segmentation accuracy of the PointCNN, experimentally evaluated on ShapeNet Parts dataset, outperformed 14 other methods in segmenting objects belonging to seven classes, while it achieved comparable accuracy in segmentation of objects from the other nine classes. The CNN architectures have been shown to be the most accurate and efficient approaches in a recent review of deep learning techniques applied to semantic segmentation, given in [7]. The best accuracy of object segmentation achieved by seven deep learning methods reported in [4] was between 63 and 96%, depending on the object class. However, manual annotation of large datasets required for training neural networks is a time-consuming and delicate problem. Therefore, [PERSON] et al. [8] proposed a novel active learning method capable of segmenting massive geometric datasets into accurate semantic regions that grants a good accuracy vs. efficiency trade-off. The ShapeNet Parts dataset was annotated using this approach. The goal of this approach (reducing human work required for the annotation of large datasets) is also the main motivation of the component association method proposed in this paper. The framework [8] achieved the annotation of large-scale datasets by cycling between manually annotating the regions, automatically propagating these annotations across the rest of the shapes, manually verifying both human and automatic annotations, and learning from the verification results to improve the automatic propagation algorithm. Although the approach proposed in [8] included the propagation of component labels from a small object set to a larger set, the focus of their research was on the whole iterative annotation process. A kind of continuation of the research [8] was given in [9]. The method also propagates labels from a small subset to a large dataset by global optimization analogous to the approach proposed in [10], which is based on conditional random fields. Our research, on the other hand, focuses on efficient detection of the target component on a query object given a small annotated set of representative objects, which could allow database expansion in real time. An automatic approach to achieve semantic annotation of 3D models was proposed in [11]. The approach extracts concave and convex features as the cues for object decomposition into structural parts. By analyzing the position, shape, size, and configuration of the structural parts, the semantic category is assigned to each of them. The proposed methodology resembles the approach proposed by our paper, but it is applied in the semantic annotation of architectural buildings; therefore, the descriptors are adapted to that application, e.g., relative height, volume, dimension ratio, form mode, etc. The final assignment of the semantic label to a part in [11] was performed by the decision tree and the adapted support vector machine, while in this paper, three variants of assigning the label based on the CAG are proposed. Analogous to the related methods discussed in this section, our method also performs segmentation of complete 3D object models.