# VFLD: Voxelized Fractal Local Descriptor
## 4 Experimentation
### Datasets  
Our experiments involved four different datasets. First, we used ModelNet10 [33], which is a state-of-the-art 3D object dataset and was chosen to test our approach regarding object recognition tasks, which are one of the most fundamental tasks in computer vision. We then created our own synthetic benchmark that features simple figures. This was performed to assess the fundamental accuracy of the proposal on a controlled and heterogeneous set of data. Finally, we used ScanNet [34] and ViDRILO [35] datasets for the three-dimensional registration experiments. These datasets were chosen to test VFLD on a point cloud registration setup, which is also a common task within the computer vision topic. It is worth noting that the chosen datasets provide results for different tasks that can be approached using point cloud feature descriptors, setting a realistic and insightful testing environment for the proposal.  
We now provide some details on the datasets.  
ModelNet10 [33] is a dataset comprising thousands of computer-assisted design objects that span across ten different categories, and it is widely used in the context of three-dimensional object classification. Nonetheless, we adopted it for our experiments as it provides great variability of categories and instances. Specifically, we used a subset of 10 random objects of each category, thus gathering a dataset of 100 different objects. As the objects are distributed as a mesh format, we converted them to point clouds following the protocol used in [36]. The sampling resolution was 3000 points per object. All the objects were normalized and thus all had a maximum size of 100. Random samples of this dataset can be seen in Figure 6. The density of the resulting dataset was 1.47.  
Simple Figure is the custom dataset built for the study. It features 10 different simple geometric figures: sphere, arrow, box, cone, cylinder, icosahedron, Moebius strip, octahedron, tetrahedron, and torus. All these synthetic figures were also sampled, and 3000 points were thus obtained for each one. Furthermore, they underwent the same normalization process as that explained for the previous dataset. To augment the dataset, 10 random rotations were applied to each figure, finally comprising 100 different samples of 10 different figures. Note that this dataset includes figures with high levels of symmetry. Random samples of this dataset can be seen in Figure 7. The density of the resulting dataset is 1.18.  
ScanNet [34] is a dataset comprising RGB-D scans representing 707 different indoor real-world environments, captured by a Structure sensor. Figure 8 shows a number of random scene examples. It also provides several additional items of information (such as semantic segmentation labels, camera parameters, and others), although we are only interested in scene point clouds for our purposes here. ScanNet was not initially developed for registration, as it contains only complete views of rooms. Consequently, we had to implement a fragmentation protocol, intending to obtain consecutive overlapping parts of each scene. This process is described in Section 4.9.  
Figure 6: Random samples of the ModelNet10 dataset.  
Figure 7: Random samples of the Simple Figures dataset.  
ViDRILO [35] is a dataset composed of different sequences of RGB-D data captured by a Kinect 1 sensor, which is a structured light 3D camera. The sequences depict different scenes at the University of Alicante and are explicitly created to benchmark point cloud registration methods. The dataset also provides semantic labels of places and objects, which we do not use. Some examples of this dataset are shown in Figure 9.