# A Comparison of Segmentation Methods for Semantic OctoMap Generation
## 5 Discussion  
The conducted experiments enabled a comparison of the quality of OctoMaps generated with additional semantic information derived from image segmentation. Our findings demonstrate that MobileNetV4, the most recent and lightweight of the tested encoders, delivered the most precise environment representation while achieving the lowest peak GPU usage and shortest inference time. In combination with the FPN model, this configuration produced maps with approximately 0.80 accuracy of voxel classification on the Validation scene and over 0.72 on the Test scene, with an average inference time below 7 ms. However, switching to DeepLabv3+ led to a further reduction in mean inference latency to 5.6 ms with a marginal trade-off in accuracy. We subsequently analyzed the impact of reducing image resolution on performance and quality. Halving both image dimensions accelerated inference by over 2.5 times and reduced peak GPU usage while maintaining nearly identical map quality. Maps generated using U-Net with ResNet-34 encoder at reduced resolution exhibited negligible accuracy loss compared to optimal full-resolution models while achieving a mean inference time of only 4.3 ms.  
Additionally, we compared the quality of maps generated by models trained on masks produced by a domain-specific segmentation neural network versus masks created by a general-purpose model (specifically Grounding DINO and SAM2). When the latter were used for training, only around 50% of voxels were correctly classified. Furthermore, this approach produced inconsistent semantic labels in more than 20% of voxels (almost twice  
Figure 10: Visual comparison performed on the Test scene between ground truth annotations and maps generated by the models that achieved the highest accuracy on the Test scene in each experimental setup. as much as models trained with masks from a specialized neural network), introducing substantial uncertainty in the map. In summary, we evaluated robustness in multiple ways within the scope of this study, including:  
**Data Augmentation:**: To improve generalization and simulate varied conditions, all models were trained with significant data augmentation. This included random horizontal flips, mild color jitter to simulate different lighting, and random rotations to increase spatial diversity. **Performance on Unseen Data:**: The models were evaluated on different trajectories from the training and validation data, demonstrating the ability to generalize to new and unseen areas of the campus environment. **Model Encoder Combinations:**: We conducted a comprehensive evaluation of robustness across multiple encoder-decoder architectures with various backbone configurations. The primary focus of this comparison was to assess the semantic segmentation quality, latency, memory footprint, and GPU utilization for each model and encoder combination. **Variable Input Resolution:**: Analysis of the trade-off between model accuracy and real-time performance by testing at different input image resolutions (960 \(\times\) 608 vs. 480 \(\times\) 304). Our results show that reducing image resolution can accelerate inference by over 2.5 times with only a negligible loss in map quality, a critical robustness test for resource-constrained robotic systems. **Annotation Quality:**: We tested the training pipeline's robustness to the quality of annotation data by comparing models trained on highly specific, domain-adapted masks (City-scapes fine-tuned SegFormer-B5) versus those trained on masks from a general-purpose model (Grounding DINO, SAM2). The results demonstrated that the quality of annotation is critical, as the general-purpose masks led to a significant drop in performance, with almost twice the number of unclassified voxels. In the future, we aim to experiment with semantic mapping for a variety of robotic applications.