## 3 Results  
The combined, but not yet projected, test set predictions for all networks are provided in Figure 7. The metrics and statistics calculated from these predictions are provided in Table 3. It is important to note that the values presented in this table are not directly comparable unless they originated from the same input point cloud modality. This is \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Architecture** & **\# Params** & **Max Epoch** & **Opt/LR** & **Batch Size** & **Epoch Time** & **VRAM Mean** \\ \hline L3D & 24.3 M & 150 & SGD/\(1\times 10^{-2}\) & 16 & 8.8 min & 29 GB \\ H3D-Flat & 24.5 M & 150 & SGD/\(1\times 10^{-2}\) & 64 & 7.2 min & 21 GB \\ H3D-DSM & 24.5 M & 150 & SGD/\(1\times 10^{-2}\) & 64 & 6.9 min & 20 GB \\ HSD-L3D & 125.7 M & 150 & SGD/\(1\times 10^{-2}\) & 12 & 13.5 min & 41 GB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selected subset of model characteristics and computational requirements. The slight differences between H3D-Flat and H3D-DSM epoch time and memory usage are due to the different sizes of neighbors used during convolution. The reported value of HSD-L3D’s model parameter count includes the decoding sections of the unimodal network streams. because predictions made against, for example, the lidar point cloud modality are not localized to the same set of points as the HSPC modality. A caveat to this is that all HSPC predictions are directly comparable because they share the same set of point locations albeit with different \(Z\) values. In addition, note, in Table 3, the statistics for HSD-L3D are presented against the canonical point locations, lidar point locations, and structural HSPC point locations. This is possible because the canonical set of point locations can easily be decomposed into the lidar and hyperspectral contributions as a result of the pre-computed graph pyramid input (Section 2.2.3). We present these metrics and statistics to illustrate the models' performance against their input, in the proceeding paragraphs, we also present their projected predictions for inter-model comparison. First, we compare the two predictions from L3D and HSD-L3D (Lidar) against the lidar point cloud locations. The marginal balanced and pixel accuracy improvements of the HSD-L3D (Lidar) prediction over the L3D prediction provides weak evidence that the incorporation of the structural HSPC information within the multimodal network is effective. Further, we note the differences in the per-class accuracy against the vehicle class. The L3D network moderately outperformed the HSD-L3D, and thus, we recognize that the incorporation of the structural point cloud information within the multimodal network, while effective overall, could introduce some ambiguity toward specific classes. In this case, a correct prediction on the vehicle class may be the most reliant on structural information (in relation to the other classes) for proper prediction given the highly variable spectral information vehicles present. We may also compare the three predictions from the H3D-Flat, H3D-DSM, and HSD-L3D (HSPC) models against the HSPC point locations. The overall under-performance of the H3D-Flat network is directly attributed to the 2D nature of the spectral HSPC input. This modality does not provide structural information and results in many of the KPConv kernels/filters going under-utilized. Despite this, the model was still able to produce non-trivial performance. Interestingly, the rooftop parking garage structure on the left-hand  
Figure 7: Combined test sample predictions over the entire test set area for all models. Note, predictions are shown against the point locations against which they were generated; denoted in parenthesis in subplot naming. **(Row 1, Column 1)** Ground truth point cloud labels for the structural HSPC. **(Row 1, Column 2)** Ground truth point cloud labels for the lidar modality. **(Row 2, Column 1)** L3D prediction. **(Row 3, Column 1)** H3D-Flat prediction. **(Row 4, Column 1)** H3D-DSM prediction. **(Row 2, Column 2)** HSD-L3D prediction.