# Detection of Household Furniture Storage Space in Depth Images
## 6 Discussion  
The generated synthetic dataset was successfully used to train two neural networks adapted from indoor object detection to the storage space detection. This indicates that the combination of the proposed methods for automatically labeling empty storage volumes and generating synthetic scenes achieved its goal. Comparing the precision results of the storage volume detectors in the synthetic dataset (Table 5), we found that the results for both detectors are similar at the required IoU of 0.25. The difference is notable at the required IoU of 0.5 in favor of the FCAF3D storage volume detector. For both IoU settings, the FCAF3D detector has significantly higher recall than VoteNet. Therefore, the detector trained on FCAF3D achieved better overall precision and recall results on the synthetic dataset. However, the detector trained on VoteNet achieved 18% higher precision on the real dataset, and FCAF3D has 8% higher recall. Since the purpose of the proposed method is to detect household furniture storage in real depth images, the results of VoteNet detector are further discussed. Table 5 shows that the detection of storage volumes performs slightly better on the bookshelf class compared to other furniture classes. The visual results of storage volume detection in the synthetic dataset show similarity in the number, size, and position of predictions relative to the ground truth labels. \begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|} \hline \multicolumn{2}{|c|}{**Environment**} & \multicolumn{3}{c|}{**Home 1**} & \multicolumn{3}{c|}{**Home 2**} & \multicolumn{3}{c|}{**Home 1**} & \multicolumn{3}{c|}{**Office 1**} & \multicolumn{3}{c|}{**Office 2**} \\ \hline \multirow{4}{*}{**Room**} & \multirow{2}{*}{**NN**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**ResNet**} & \multirow{2}{*}{**ResNet**} & \multirow{2}{*}{**ResNet**} & \multirow{2}{*}{**ResNet**} & \multirow{2}{*}{**ResNet**} & \multirow{2}{*}{**ResNet**} & \multirow{2}{*}{**ResNet**} & \multirow{2}{*}{**ResNet**} & \multirow{2}{*}{**ResNet**} \\  & & & & & & & & & & & & & & & & \\ \hline \multirow{4}{*}{**0.25**} & \multirow{4}{*}{Precision [\%]} & VoteNet & **37.73** & **56.44** & **43.22** & **33.64** & **40.72** & 73.10 & 43.38 & **44.50** & **53.21** & 42.85 & **55.35** & **42.22** \\  & & FCAF3D & 25.15 & 9.66 & 37.75 & 27.25 & 26.11 & **74.58** & **45.72** & 29.30 & 18.82 & **33.41** & 20.89 & 24.17 \\ \cline{2-11}  & \multirow{4}{*}{Recall [\%]} & VoteNet & 64.71 & **81.82** & 74.42 & 64.81 & 60.00 & 100.60 & 62.50 & **64.31** & 61.82 & 63.73 & **91.18** & 66.67 \\ \cline{2-11}  & & FCAF3D & **82.35** & 72.73 & **76.74** & **85.19** & 60.00 & 100.78 & **78.41** & 72.55 & **65.45** & **72.58** & 67.56 & **75.42** \\ \hline \multirow{4}{*}{**0.50**} & \multirow{4}{*}{Precision [\%]} & VoteNet & 0.84 & 0.00 & 0.83 & 4.61 & 0.00 & **34.00** & 0.73 & **20.22** & 1.88 & 6.32 & **22.20** & 5.25 \\  & & FCAF3D & 7.72 & 0.25 & 21.83 & **16.09** & **2.25** & 72.50 & 19.38 & 17.66 & **2.31** & **27.88** & 8.27 & **9.90** \\ \cline{1-1} \cline{2-11}  & \multirow{4}{*}{Recall [\%]} & VoteNet & 5.88 & 0.00 & 11.63 & 20.37 & 0.00 & 60.00 & 6.82 & 99.29 & 99.21 & 51.24 & **52.94** & 21.04 \\ \cline{1-1}  & & FCAF3D & **23.53** & **9.09** & **37.21** & **51.85** & **35.00** & **80.00** & **40.91** & **45.10** & **20.00** & **40.02** & 41.18 & **41.46** \\ \hline \end{tabular}
\end{table} 
Table 6: Results of evaluation of the storage volume detectors on the real dataset. Bold values represent higher precision or recall, which indicates a better result. Furthermore, by observing the results obtained on the real dataset, and in particular comparing the scenes of the same bookshelf in two settings in room Office 1--an empty bookshelf and a bookshelf with a specific clutter--we can see that better results were achieved for a bookshelf with moderate clutter. Qualitative results obtained on the real dataset indicate that the storage volume detector achieves better results for images taken closer to the furniture.