\section{Chamfer Distance Distributions} \label{sec:appendix_cd}

The Chamfer distance can be used to calcualte the distance between two sets of points, e.g., point clouds. Given two sets of points $X$ and $Y$, the Chamfer distance CD($X$, $Y$) is:
\begin{align}
    \text{CD}(X, Y) = \frac{1}{|X|}\sum_{\mathbf{x}\in X}\min_{\mathbf{y} \in Y}||\mathbf{x} - \mathbf{y}|| + \frac{1}{|Y|}\sum_{\mathbf{y}\in Y}\min_{\mathbf{x} \in X}||\mathbf{y} - \mathbf{x}||,
\end{align}
where $|\cdot|$ denotes set cardinality. We determine the uniqueness of the generative model by calculating  the Chamfer distance between each generated sample and the full training set. 
We consider the training sample that minimizes the Chamfer distance to be the sample that most closely resembles the generated sample.
In figure \ref{fig:appendix_cd}, we report the minimum Chamfer distance between the generated samples and the training set.
\begin{figure}[h!]
    \centering
    \includegraphics[width=\linewidth]{figures/appendix/chamfer_distances.png}
    \caption{Minimum Chamfer distances between the generated samples and the training set for the samples with (left) and without (right) an M1 occlusion.}
    \label{fig:appendix_cd}
\end{figure}