  For effective structural damage assessment, the instances of damages need to
be localized in the world of a 3D model. Due to a lack of data, the detection
of structural anomalies can currently not be directly learned and performed in
3D space. In this work, a three-stage approach is presented, which uses the
good performance of detection models on image level to segment instances of
anomalies in the 3D space. In the detection stage, semantic segmentation
predictions are produced on image level. The mapping stage transfers the
image-level prediction onto the respective point cloud. In the extraction
stage, 3D anomaly instances are extracted from the segmented point cloud. Cloud
contraction is used to transform cracks into their medial axis representation.
For areal anomalies the bounding polygon is extracted by means of alpha shapes.
The approach covers the classes crack, spalling, and corrosion and the three
image-level segmentation models TopoCrack, nnU-Net, and DetectionHMA are
compared. Granted a localization tolerance of 4cm, IoUs of over 90% can be
achieved for crack and corrosion and 41% for spalling, which appears to be a
specifically challenging class. Detection on instance-level measured in AP is
about 45% for crack and spalling and 73% for corrosion.
