Extended preparation of data sets

Signed-off-by: Jim Martens <github@2martens.de>

body.tex

@@ -656,8 +656,24 @@ Wordnet ID and searching for a fitting COCO class.
 
 The ground truth for SceneNet RGB-D is stored in protobuf files
 and had to be converted into Python format to use it in the
-codebase. Only ground truth instances that had a matching
+codebase. The trajectories are not sorted inside the protobuf,
-COCO class were saved, the rest discarded.
+therefore, the first action was to sort them. For each trajectory,
+all instances are stored independently of the views in the
+trajectory. Therefore, the trajectories and their respective
+instances were looped through and all
+background instances and those without corresponding COCO class were
+skipped. The rest was stored in a dictionary per trajectory.
+Subsequently, all views of the trajectory were traversed and
+for every view all stored instances were looped through.
+For every instance, the segmentation map was modified by
+setting all pixels not having the instance ID as value to zero
+and the rest to one. If no objects were found then that instance
+was skipped. In the other case a copy of its data from the
+aforementioned dictionary plus the bounding box information was
+stored in a list of instances for that view. The list of instances
+per view was added to a list of such lists for the trajectory.
+Ultimately this list of lists was added to a global list across
+all trajectories: a list of lists of lists.
 
 \section{Replication of Miller et al.}