# Detection of Household Furniture Storage Space in Depth Images
## 5 Results  
The storage space detectors, trained on VoteNet and FCAF3D neural networks, were evaluated with two test datasets: synthetically generated and real depth images. The evaluation was performed by computing the 3D volume intersection over the union (IoU) values of ground truth and prediction boxes, where thresholds 0.25 and 0.5 were used to calculate the average recall for generation and average precision for detection, as described in [2]. The proposals were post-processed using a 3D non-maximum suppression (NMS) IoU threshold of 0.1 and a confidence threshold of 0.75. The storage volume detector was tested on the synthetic dataset in two ways: on the entire evaluation dataset and on a sub-dataset consisting of a bookshelf and negative scenes. The training of the storage volume detector was performed on a synthetic dataset, half of which consisted of scenes with bookshelves, and the remaining scenes of the dataset were evenly distributed among the other classes. Therefore, the trained network was tested separately in scenes with bookshelves and in scenes with cabinets, desks, and tables. The results of this test, together with the testing results on the real dataset, can be found in Table 5. \begin{table}
\begin{tabular}{|c|c|c|c c c|c|} \hline \multirow{2}{*}{**IoU Threshold**} & \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**NN**} & \multicolumn{4}{c|}{**Synthetic Dataset**} & \multirow{2}{*}{**Real Dataset**} \\ \cline{3-3} \cline{5-8}  & & & **DS1** & & **DS2** & **DS** \\ \hline \multirow{4}{*}{0.25} & \multirow{2}{*}{Precision [\%]} & VoteNet & 44.09 & 39.63 & 43.66 & 42.22 \\  & & FCAF3D & 45.47 & 57.69 & 47.72 & 24.17 \\ \cline{2-8}  & & VoteNet & 62.91 & 57.46 & 62.29 & 66.67 \\  & & FCAF3D & **70.83** & **73.31** & **71.41** & **75.42** \\ \hline \multirow{4}{*}{0.50} & \multirow{2}{*}{Precision [\%]} & VoteNet & 13.85 & 13.99 & 14.03 & 5.25 \\  & & FCAF3D & **29.17** & **43.27** & **32.23** & **9.9** \\ \cline{1-1} \cline{2-8}  & & VoteNet & 35.77 & 35.78 & 35.91 & 21.04 \\ \cline{1-1}  & & FCAF3D & **42.97** & **55.24** & **45.82** & **41.46** \\ \hline \end{tabular}
\end{table} 
Table 5: Results of evaluation of the storage space detectors on the synthetic and real datasets. NMS IoU = 0.1; confidence threshold = 0.75. DS1 = bookshelves and negative scenes. DS2 = cabinets, desks, and (empty) tables. DS = complete dataset. Bold values represent higher precision or recall, which indicates a better result. Figure 11: Ground truth annotation of the household furniture. (**a**) A bookshelf captured by an RGB-D camera. (**b**) A bookshelf with ground truth annotation. Table 6 shows evaluation results of storage space detection on the real dataset per room where scenes were captured. Examples of qualitative results of storage volume detection in real images are shown in Figures 12-14. Figure 12: Examples of storage volumes in depth images for different furniture classes: (**a,b**) book-shelf, (**c,d**) cabinet, (**e,f**) desk, and (**g,h**) table. The left column shows ground truth annotation of storage volumes in the selected furniture classes. The right column shows the storage volumes predicted in these furniture models by the VoteNet detector. Figure 13: Examples of storage volumes in depth images contained in a real dataset. The left column shows ground truth annotation of storage volumes in the selected furniture classes. The right column shows the storage volumes predicted in these furniture models by the VoteNet detector. **(a,b)** Show a close-up view of a bookshelf, (**c,d**) Show a bookshelf seen from a distance, (**e,f**) Show a close-up view of a cabinet, (**g,h**) Show a cabinet seen from a distance. Figure 14: Examples of storage volumes in depth images contained in a real dataset. The left column shows ground truth annotations of storage volumes in the selected furniture classes. The right column shows the storage volumes predicted in these furniture models by the VoteNet detector.