# Integrated Gradients for Feature Assessment in Point Cloud-Based Data Sets
## 5 Conclusions  
Integrated gradients can provide a deeper insight into why a model reaches a prediction; however, calculating this interpretation of attributions is computationally expensive and requires storing vast tensors to keep track of all the gradients. This limits the visualization to medium-point clouds or models with few parameters. However, the technique can be applied to quantitatively evaluate how much an input modality contributes to a prediction, this can be used to evaluate whether input modalities are useful for a prediction. New LiDAR sensors that provide additional data such as humidity or temperature have become available. Here, integrated gradients can significantly cut down training time as it enables all modalities to be fed into the model during training and after the attributions are found to provide a relative impact score for each modality. Otherwise, the network has to be run for every single modality separately. As the run time of the proposed integrated gradient model is less than 25% of the training time, this is time-efficient when evaluating at least three modalities. Additionally, the technique can be useful in evaluating which data fusions are beneficial, especially when a large amount of data sources are available.  
Overall, applying integrated gradients on point clouds provided insights into the models' prediction as it allowed an understanding of why some of the buildings were predicted correctly and others were not. These insights enable the designers of these systems to reduce the memory size of the required point cloud, remove unnecessary sensors, and reduce the number of parameters required by the neural network to reach a prediction. Due to the large memory requirement, the application remains limited and further advances in the field of explainable AI are necessary to obtain a better understanding of how deep learning models operate.  
Conceptualization, M.S.; methodology, M.S.; software, M.S.; validation, M.S.; writing--original draft preparation, M.S.; supervision, C.M., A.R.; project administration, C.M., A.R.; funding acquisition, C.M., A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **RGB** & **Coordinates** \\ \hline man-made terrain & 0 & 0.5498444 \\ \hline natural terrain & 6.0026236 \(\times\) 10\({}^{-6}\) & 0.9657749 \\ \hline high vegetation & 9.214191 \(\times\) 10\({}^{-6}\) & 0.91953284 \\ \hline low vegetation & 9.287189 \(\times\) 10\({}^{-6}\) & 0.8924127 \\ \hline buildings & 4.39797 \(\times\) 10\({}^{-6}\) & 0.63412595 \\ \hline hard scape & 4.5726824 \(\times\) 10\({}^{-6}\) & 0.6900027 \\ \hline scanning artefacts & 1.1220334 \(\times\) 10\({}^{-5}\) & 1 \\ \hline cars & 6.3846205 \(\times\) 10\({}^{-6}\) & 0.8025473 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Normalized and squared class-wise attributions for 10 iterations on the Semantic 3D dataset.