# Semantic Component Association within Object Classes Based on Convex Polyhedrons
## 8 Conclusions  
The aim of the paper was to investigate how accurate semantic association can be achieved without extensive training and annotation of a large amount of data and if such an approach could be time effective for real-time applications. The target application of the proposed approach was in robotics, where it could be used in combination with an object detection and 3D reconstruction module. The task of this module would be to detect objects of a particular class in an RGB-D image or a LiDAR scan and to reconstruct their full 3D model. The proposed approach could then be used to associate the components of the reconstructed object, with the corresponding components of representative object models. In order to be applicable in real-world scenarios, the robot perception system must be able to cope with cluttered scenes, where the target objects are partially visible and appear in various poses. Augmentation of the proposed approach with a method that would detect objects of a given class in complex scenes and perform their 3D reconstruction is our first choice for the continuation of the presented research.  
Since the application of neural networks in semantic segmentation and object classification was justified by the reported accuracy in related research, training a neural network for general-purpose component segmentation and matching is an interesting subject for future research. In order to apply a neural network for solving the problem defined in this paper, a neural network should be trained using various datasets in order to learn a generic criterion for possible semantic association between model components. After being trained, the network would be applied for detection and association of the components of another unannotated dataset.  
In this paper, a method for semantic association of the object components with the relatively small number of the annotated representative objects was proposed. The method was based on construction of the component association graph, whose nodes represented the object segments. Segments were approximated by convex polyhedrons. The CAG edges were assigned a semantic correspondence likelihood measure between the segments, which took into consideration both segments and their neighborhood similarity. The final association was performed by one of the three proposed methods based on the component association graph. The evaluation of the proposed approach on a subset of the ShapeNet Part benchmark dataset yielded interesting results, which indicated that with a rather small number of annotated representatives, the identification of a target component with accuracy measured by IoU greater than 0.6 could be achieved in only a few seconds.  
**Author Contributions:** Conceptualization, R.C.; data curation, P.D.; formal analysis, P.D. and R.C.; funding acquisition, R.C.; investigation, P.D. and R.C.; methodology, P.D. and R.C.; project administration, R.C.; software, P.D., I.V., and R.C.; supervision, R.C.; validation, R.C.; visualization, R.C.; writing, original draft, P.D. and R.C.; writing, review and editing, P.D. and R.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been fully supported by the Croatian Science Foundation under the project number IP-2014-09-3155.  
**Acknowledgments:** We are especially grateful to [PERSON] from Stanford University for kindly providing us the mapping between ShapeNet dataset meshes and point cloud model names and helping us with other dilemmas regarding ShapeNet.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.