# A Comparison of Segmentation Methods for Semantic OctoMap Generation
## 4 Results
### Semantic OctoMaps Validation
In the test scenario, the relatively low NC \(\approx\) 0.1239 indicates that U-Net+MobileNetV4 produces one of the most complete semantic maps. As reported in Table 10, at lower resolution, U-Net with ResNet-34 attained the lowest unclassified-voxel rate (NC \(\approx\) 0.1202) on the test split, producing the most complete occupancy map-crucial for robotic scenarios that depend on continuous environment reconstruction. Moreover, on the test scene, U-Net + ResNet-34 achieves the lowest unclassified-voxel rate (NC \(\approx\) 0.1202), providing the most complete semantic coverage even at reduced resolution, with only marginal losses in overall accuracy and weighted \(F_{1}\). In practice, downscaling can significantly reduce computational cost while sacrificing only a few percentage points of filtered accuracy and \(F_{1}\)  
In Table 11, we show that masks generated by GroundingDINO and SAM2 in their current implementation demonstrate inadequate semantic precision, leading to an increase in NC of approximately 20-30% and a reduction in wF1\({}_{f}\) of roughly 10-15%. For instance, on the validation scene, DeepLabv3+ + EfficientNet-B2 achieves ACC \(\approx\) 0.5648, wF1\({}_{f}\)\(\approx\) 0.8623, and NC \(\approx\) 0.2941, while U-Net + ResNet-34 records ACC \(\approx\) 0.5228, wF1\({}_{f}\)\(\approx\) 0.8270, and NC \(\approx\) 0.3069. These results demonstrate that GroundingDINO+SAM2 masks lack the precision necessary for reliable semantic mapping. \begin{table}
\begin{tabular}{l l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Encoder**} & \multicolumn{6}{c}{**Validation Scene**} & \multicolumn{6}{c}{**Test Scene**} \\  & & **ACC** & **ACC\({}_{f}\)** & **wF1** & **wF1\({}_{f}\)** & **NC** & **ACC** & **ACC\({}_{f}\)** & **wF1** & **wF1\({}_{f}\)** & **NC** \\ \hline DeepLabv3+ & EfficientNet-B2 & **0.797** & **0.933** & **0.861** & **0.938** & **0.146** & **0.734** & **0.837** & **0.797** & **0.856** & 0.123 \\ \hline U-Net & ResNet-34 & 0.781 & 0.930 & 0.850 & 0.936 & 0.160 & 0.733 & 0.834 & 0.796 & 0.853 & **0.120** \\ \hline \hline \endIn Figures 6-8, we compare the per-class F1-scores of the top-performing models across validation and test scenes. The model based on DeepLabv3+ with EfficientNet-B2 achieves high F1-scores of 0.91 for the Road class, 0.87 for Building, and 0.76 for Tree, demonstrating consistently strong performance across both validation and test scenes. The U-Net with MobileNetV4, despite its lightweight architecture, delivers stable performance across both validation and test scenes. On the test scene, it attains F1-scores of 0.91 for Road, 0.87 for Building, and 0.76 for Tree, matching the performance of DeepLabv3+ with EfficientNet-B2. On the validation scene, it maintains strong results with scores of 0.92, 0.88, and 0.76 for the same classes. The U-Net model with a ResNet-34 encoder shows slightly lower performance across most categories, reaching F1-scores of 0.86 for Road, 0.79 for Building, and 0.75 for Tree. In contrast, the classes Traffic objects and Grass remain the most challenging for all evaluated models, with F1-scores consistently falling below 0.3, particularly when models are trained on SAM2-derived annotations. This observation \begin{table}
\begin{tabular}{l l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Encoder**} & \multicolumn{4}{c}{**Validation Scene**} & \multicolumn{4}{c}{**Test Scene**} \\  & & **ACC** & **ACC\({}_{f}\)** & **wF1** & **wF1\({}_{f}\)** & **NC** & **ACC** & **ACC\({}_{f}\)** & **wF1** & **wF1\({}_{f}\)** & **NC** \\ \hline DeepLabv3+ & EfficientNet-B2 & **0.565** & **0.800** & **0.697** & **0.862** & **0.294** & 0.459 & 0.612 & 0.592 & 0.706 & 0.249 \\ \hline U-Net & ResNet-34 & 0.523 & 0.754 & 0.655 & 0.827 & 0.307 & **0.532** & **0.683** & **0.653** & **0.758** & **0.220** \\ \hline \hline \end{tabular}
\end{table}