# Semantic Component Association within Object Classes Based on Convex Polyhedrons
## 7 Experimental Evaluation  
The proposed approach was experimentally evaluated using the ShapeNet 3D model database [5]. A part of this dataset is dedicated to testing methods that segment 3D models into semantic parts. This dataset consists of 16 subsets, each representing one object class. Each of these subsets is subdivided into training, validation, and test subsets, where the training subsets are significantly larger than the validation and test subsets. The dataset was originally designed to be used in the following way: the evaluated method should be trained using a manually annotated training dataset and the associated validation dataset and tested using the corresponding test dataset. However, in this paper, we investigated a different paradigm. We wanted to test the ability of the proposed approach to (i) segment unannotated 3D models of a particular object class into segments and establish associations between these segments according to the similarity of their shape, size, and geometric arrangement and (ii) to use the established associations to identify a user selected component in all models given a small set of manually annotated representatives. In order to automatize the experiments that require a user selection, instead of manually annotating the representative set in each experiment, we used the ground truth annotations available for every model in the dataset. Since the target application of the proposed approach was facilitating robotic operations, we selected six object classes from the considered dataset, which could be associated with an exactly defined robot task: mugs, knives, bags, lamps, caps, and laptops. For the mugs, knives, and bags, the target component was the handle for the robot grasping task. For the lamps, the target component was the light bulb for the replacement task. The target component of the cap was the peak for the dressing task, and the target component of the laptop was the screen for the opening and closing task. In the case of all classes except the lamps, we used the original annotations of the ShapeNet dataset, where the object handle, peak, and screen were annotated as one of the semantic categories. In the case of lamps, we selected a subset of 210 lamps with clearly distinguishable light bulbs and annotated the light bulbs manually. The following experimental procedure was applied to each of the six considered object classes, with the parameter values given in Table 1.  
1. Every model was segmented into convex and concave segments using the approach proposed in [16]. 2. Segment merging was performed by the procedure described in Appendix A. 3. Every segment was represented by a CTI descriptor. Analogously to [3,19], we used a convex template consisting of \(n_{d}=66\) unit vectors uniformly distributed on the unit sphere. 4. The TRED descriptor, described in Section 4, was computed for every segment pair of every model. 5. The neighbor clusters were identified and assigned to every segment. 6. The CTI descriptor of every segment was projected onto the corresponding latent space by (2). 7. The SCLM proposed in Section 4 was computed between each of two segments of different models in the model set. 8. A CAG was created. The nodes of this graph represented all segments of all models in the model set. Each node was connected to at most 100 most similar segments, according to the SCLM computed in Step 7. 9. A small number of representative objects was selected by the method proposed in Section 6. This number was \(\leq\)10% of all models in the considered model set. 10. A target component was annotated on the representative objects. In a practical application, this step would be performed manually by a human expert. In the reported experiments, the ground truth annotations, which were available for all models, were assigned to the representative object models. 11.