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thesis/Main.bbl

@@ -1940,8 +1940,8 @@
       \strng{authornamehash}{c4d64624ede10e1baa66843e963d7c13}
       \strng{authorfullhash}{c4d64624ede10e1baa66843e963d7c13}
       \strng{authorfullhashraw}{c4d64624ede10e1baa66843e963d7c13}
-      \field{sortinit}{6}
-      \field{sortinithash}{b33bc299efb3c36abec520a4c896a66d}
+      \field{sortinit}{5}
+      \field{sortinithash}{20e9b4b0b173788c5dace24730f47d8c}
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       \field{labeltitlesource}{title}
       \field{title}{ODDS Library}

thesis/Main.tex

@@ -505,25 +505,26 @@ In the anomalous experiments, the artificial smoke machine appears to have been
 
 The \rev{Figures}~\ref{fig:data_screenshot_pointcloud}~and~\ref{fig:data_screenshot_camera} show an representative depiction of the environment of the experiments as a camera image of the IR camera and the point cloud created by the OS1 \rev{LiDAR} sensor at practically the same time.
 
-\fig{data_screenshot_pointcloud}{figures/data_screenshot_pointcloud.png}{Screenshot of 3D rendering of an experiment's point cloud produced by the OS1-32 \rev{LiDAR} sensor without smoke and with illumination (same frame and roughly same alignment as \rev{Figure}~\ref{fig:data_screenshot_camera}). Point color corresponds to measurement range and axis in center of figure is the \rev{LiDAR}'s position.}
-\fig{data_screenshot_camera}{figures/data_screenshot_camera.png}{Screenshot of IR camera output of an experiment without smoke and with illumination (same frame and roughly same alignment as \rev{Figure}~\ref{fig:data_screenshot_pointcloud})}
+\figc{data_screenshot_pointcloud}{figures/data_screenshot_pointcloud.png}{Screenshot of 3D rendering of an experiment's point cloud produced by the OS1-32 \rev{LiDAR} sensor without smoke and with illumination (same frame and roughly same alignment as \rev{Figure}~\ref{fig:data_screenshot_camera}). Point color corresponds to measurement range and axis in \rev{the center of the figure marks} the \rev{LiDAR}'s position.}{width=.9\textwidth}
+
+\figc{data_screenshot_camera}{figures/data_screenshot_camera.png}{Screenshot of IR camera output of an experiment without smoke and with illumination (same frame and roughly same alignment as \rev{Figure}~\ref{fig:data_screenshot_pointcloud})\rev{.}}{width=.9\textwidth}
 
 Regarding the dataset volume, the 10 normal experiments ranged from 88.7 to 363.1 seconds, with an average duration of 157.65 seconds. At a capture rate of 10 frames per second, these experiments yield 15,765 non-degraded point clouds. In contrast, the 4 anomalous experiments, including one stationary experiment lasting 11.7 seconds and another extending to 62.1 seconds, averaged 47.33 seconds, resulting in 1,893 degraded point clouds. In total, the dataset comprises 17,658 point clouds, with approximately 89.28\% classified as non-degraded (normal) and 10.72\% as degraded (anomalous). The distribution of experimental data is visualized in \rev{Figure}~\ref{fig:data_points_pie}.
 
-\fig{data_points_pie}{figures/data_points_pie.png}{Pie chart visualizing the amount and distribution of normal and anomalous point clouds in \cite{subter}}
+\fig{data_points_pie}{figures/data_points_pie.png}{Pie chart visualizing the amount and distribution of normal and anomalous point clouds in \cite{subter}\rev{.}}
 
-The artificial smoke introduces measurable changes that clearly separate the \textit{anomalous} runs from the \textit{normal} baseline.  One change is a larger share of missing points per scan: smoke particles scatter or absorb the laser beam before it reaches a solid target, so the sensor reports an error instead of a distance.  Figure~\ref{fig:data_missing_points} shows the resulting right–shift of the missing-point histogram, a known effect for \rev{LiDAR} sensors in aerosol-filled environments. Another demonstrative effect is the appearance of many spurious returns very close to the sensor; these near-field points arise when back-scatter from the aerosol itself is mistaken for a surface echo. The box-plot in Fig.~\ref{fig:particles_near_sensor} confirms a pronounced increase in sub-50 cm hits under smoke, a range at which we do not expect any non-erroneous measurements. Both effects are consistent with the behaviour reported in \citetitle{when_the_dust_settles}~\cite{when_the_dust_settles}.
+The artificial smoke introduces measurable changes that clearly separate the \textit{anomalous} runs from the \textit{normal} baseline.  One change is a larger share of missing points per scan: smoke particles scatter or absorb the laser beam before it reaches a solid target, so the sensor reports an error instead of a distance.  Figure~\ref{fig:data_missing_points} shows the resulting right–shift of the missing-point histogram, a known effect for \rev{LiDAR} sensors in aerosol-filled environments. Another demonstrative effect is the appearance of many spurious returns very close to the sensor; these near-field points arise when back-scatter from the aerosol itself is mistaken for a surface echo. The box-plot in \rev{Figure}~\ref{fig:particles_near_sensor} confirms a pronounced increase in sub-50 cm hits under smoke, a range at which we do not expect any non-erroneous measurements. Both effects are consistent with the behaviour reported in \rev{\cite{when_the_dust_settles}}.
 
 \fig{data_missing_points}{figures/data_missing_points.png}{Density histogram showing the percentage of missing measurements per scan for normal experiments without degradation and anomalous experiments with artifical smoke introduced as degradation.}
 
 \fig{particles_near_sensor}{figures/particles_near_sensor_boxplot_zoomed_500.png}{Box diagram depicting the percentage of measurements closer than 50 centimeters to the sensor for normal and anomalous experiments.}
 
-Taken together, the percentage of missing points and the proportion of near-sensor returns provide a concise indication of how strongly the smoke degrades our scans—capturing the two most prominent aerosol effects, drop-outs and back-scatter spikes.  They do not, however, reveal the full error landscape discussed earlier (compound errors, temperature drift, multipath, \dots), so they should be read as an easily computed synopsis rather than an exhaustive measure of \rev{LiDAR} quality. Next we will discuss how the \rev{LiDAR} scans were preprocessed before use and how we actually assigned ground-truth labels to each scan, so we could train and evaluate our quantification degradation methods.
+Taken together, the percentage of missing points and the proportion of near-sensor returns provide a concise indication of how strongly the smoke degrades our scans—capturing the two most prominent aerosol effects, drop-outs and back-scatter spikes.  They do not, however, reveal the full error landscape discussed earlier (compound errors, temperature drift, multipath, \dots), so they should be read as an easily computed synopsis rather than an exhaustive measure of \rev{LiDAR} quality. Next we will discuss how the \rev{LiDAR} scans were preprocessed before use and how we actually assigned ground-truth labels to each scan, so \rev{that} we could train and evaluate our quantification degradation methods.
 
 \newsection{preprocessing}{Preprocessing Steps and Labeling}
 
 
-As described in Section~\ref{sec:algorithm_description}, the method under evaluation is data type agnostic and can be adapted to work with any kind of data by choosing a suitable autoencoder architecture. In our case, the input data are point clouds produced by a \rev{LiDAR} sensor. Each point cloud contains up to 65,536 points, with each point represented by its \emph{X}, \emph{Y}, and \emph{Z} coordinates. To tailor the DeepSAD architecture to this specific data type, we would need to design an autoencoder suitable for processing three-dimensional point clouds. Although autoencoders can be developed for various data types, \citetitle{autoencoder_survey}~\cite{autoencoder_survey} noted that over 60\% of recent research on autoencoders focuses on two-dimensional image classification and reconstruction. Consequently, there is a more established understanding of autoencoder architectures for images compared to those for three-dimensional point clouds.
+As described in Section~\ref{sec:algorithm_description}, the method under evaluation is data type agnostic and can be adapted to work with any kind of data by choosing a suitable autoencoder architecture. In our case, the input data are point clouds produced by a \rev{LiDAR} sensor. Each point cloud contains up to 65,536 points, with each point represented by its \emph{X}, \emph{Y}, and \emph{Z} coordinates. To tailor the DeepSAD architecture to this specific data type, we would need to design an autoencoder suitable for processing three-dimensional point clouds. Although autoencoders can be developed for various data types, \rev{\cite{autoencoder_survey} observed} that over 60\% of recent research on autoencoders focuses on two-dimensional image classification and reconstruction. Consequently, there is a more established understanding of autoencoder architectures for images compared to those for three-dimensional point clouds.
 
 For this reason and to simplify the architecture, we converted the point clouds into two-dimensional grayscale images using a spherical projection. This approach—proven sucessful in related work~\cite{degradation_quantification_rain}—encodes each \rev{LiDAR} measurement as a single pixel, where the pixel’s grayscale value is determined by the reciprocal range, calculated as $v = \frac{1}{\sqrt{\emph{X}^2 + \emph{Y}^2 + \emph{Z}^2}}$. Given the \rev{LiDAR} sensor's configuration, the resulting images have a resolution of 2048 pixels in width and 32 pixels in height. Missing measurements in the point cloud are mapped to pixels with a brightness value of $v = 0$.
 
@@ -534,11 +535,11 @@ Figure~\ref{fig:data_projections} displays two examples of \rev{LiDAR} point clo
 \fig{data_projections}{figures/data_2d_projections.png}{Two-dimensional projections of two pointclouds, one from an experiment without degradation and one from an experiment with artifical smoke as degradation. To aid the readers perception, the images are vertically stretched and a colormap has been applied to the pixels' reciprocal range values, while the actual training data is grayscale.}
 
 
-The remaining challenge, was labeling a large enough portion of the dataset in a reasonably accurate manner, whose difficulties and general approach we described in \rev{Section}~\ref{sec:data_req}. Since, to our knowledge, neither our chosen dataset nor any other publicly available one provide objective labels for \rev{LiDAR} data degradation in the SAR domain, we had to define our own labeling approach. With objective measures of degradation unavailable, we explored alternative labeling methods—such as using the datas' statistical properties like the number of missing measurements per point cloud or the higher incidence of erroneous measurements near the sensor we described in \rev{Section}~\ref{sec:data_req}. Ultimately, we were concerned that these statistical approaches might lead the method to simply mimic the statistical evaluation rather than to quantify degradation in a generalized and robust manner. After considering these options, we decided to label all point clouds from experiments with artificial smoke as anomalies, while point clouds from experiments without smoke were labeled as normal data. This labeling strategy—based on the presence or absence of smoke—is fundamentally an environmental indicator, independent of the intrinsic data properties recorded during the experiments.
+The remaining challenge, was labeling a large enough portion of the dataset in a reasonably accurate manner, whose difficulties and general approach we described in \rev{Section}~\ref{sec:data_req}. Since, to our knowledge, neither our chosen dataset nor any other publicly available one provide objective labels for \rev{LiDAR} data degradation in the SAR domain, we had to define our own labeling approach. With objective measures of degradation unavailable, we explored alternative labeling methods—such as using \rev{the statistical} properties like the number of missing measurements per point cloud or the higher incidence of erroneous measurements near the sensor we described in \rev{Section~\ref{sec:data_dataset}}. Ultimately, we were concerned that these statistical approaches might lead the method to simply mimic the statistical evaluation rather than to quantify degradation in a generalized and robust manner. After considering these options, we decided to label all point clouds from experiments with artificial smoke as anomalies, while point clouds from experiments without smoke were labeled as normal data. This labeling strategy—based on the presence or absence of smoke—is fundamentally an environmental indicator, independent of the intrinsic data properties recorded during the experiments.
 
-The simplicity of this labeling approach has both advantages and disadvantages. On the positive side, it is easy to implement and creates a clear distinction between normal and anomalous data. However, its simplicity is also its drawback: some point clouds from experiments with artificial smoke do not exhibit perceptible degradation, yet they are still labeled as anomalies. The reason for this, is that during the three non-static anomalous experiments the sensor platform starts recording in a tunnel roughly 20 meters from the smoke machine's location. It starts by approaching the smoke machine, navigates close to the machine for some time and then leaves its perimeter once again. Since the artificical smoke's density is far larger near the machine it originates from, the time the sensor platform spent close to it produced highly degraded point clouds, whereas the beginnings and ends of the anomalous experiments capture point clouds which are subjectively not degraded and appear similar to ones from the normal experiments. This effect is clearly illustrated by the degradation indicators which we talked about earlier-the proportion of missing points and the amount of erroneous points close to the sensor per pointcloud-as can be seen in \rev{Figure}~\ref{fig:data_anomalies_timeline}.
+The simplicity of this labeling approach has both advantages and disadvantages. On the positive side, it is easy to implement and creates a clear distinction between normal and anomalous data. However, its simplicity is also its drawback: some point clouds from experiments with artificial smoke do not exhibit perceptible degradation, yet they are still labeled as anomalies. The reason for this, is that during the three non-static anomalous experiments the sensor platform starts recording in a tunnel roughly 20 meters from the smoke machine's location. It starts by approaching the smoke machine, navigates close to the machine for some time and then leaves its perimeter once again. Since the artificical smoke's density is far larger near the machine it originates from, the time the sensor platform spent close to it produced highly degraded point clouds, whereas the beginnings and ends of the anomalous experiments capture point clouds which are subjectively not degraded and appear similar to ones from the normal experiments. This effect is clearly illustrated by the degradation indicators which we talked about earlier\rev{--}the proportion of missing points and the amount of erroneous points close to the sensor per pointcloud\rev{--}as can be seen in \rev{Figure}~\ref{fig:data_anomalies_timeline}.
 
-\fig{data_anomalies_timeline}{figures/data_combined_anomalies_timeline.png}{Missing points and points with a measured range smaller than 50cm per point cloud over a normalized timeline of the individual experiments. This illustrates the rise, plateau and fall of degradation intensity during the anomalous experiments, owed to the spacial proximity to the degradation source (smoke machine). One of the normal experiments (without artifical smoke) is included as a baseline.}
+\fig{data_anomalies_timeline}{figures/data_combined_anomalies_timeline.png}{Missing points and points with a measured range smaller than 50cm per point cloud over a normalized timeline of the individual experiments. This illustrates the rise, plateau and fall of degradation intensity during the anomalous experiments, owed to the spacial proximity to the degradation source (smoke machine). One of the normal experiments (without artifical smoke) is included as a baseline \rev{in gray}.}
 
 Afraid that the incorrectly labeled data may negatively impact DeepSAD's semi-supervised training, we chose to manually remove the anomalous labels from the beginning and end of the anomalous experiments, for training purposes. This refinement gave us more confidence in the training signal but reduced the number of labeled anomalies. For evaluation, we therefore report results under both schemes:
 
@@ -551,11 +552,10 @@ Under both evaluation schemes all frames from normal experiments were marked as
 
 \fig{data_eval_labels}{figures/data_eval_labels.png}{Pie charts visualizing the number of normal and anomalous labels applied to the dataset per labeling scheme. A large part of the experiment-based anomalous labels had to be removed for the manually-defined scheme, since they were either subjectively clearly or possibly not degraded.}
 
-By evaluating and comparing both approaches, we hope to demonstrate a more thorough performance investigatation than with only one of the two.
+By evaluating and comparing both approaches, we hope to demonstrate a more thorough performance investigatation than with only one of the two \rev{labeling schemes}.
 
 \newchapter{experimental_setup}{Experimental Setup}
 
-
 We built our experiments starting from the official DeepSAD PyTorch implementation and evaluation framework, available at \url{https://github.com/lukasruff/Deep-SAD-PyTorch}. This codebase provides routines for loading standard datasets, training DeepSAD and several baseline models, and evaluating their performance.
 
 In the following sections, we detail our adaptations to this framework: