  The task of detecting and classifying damages in sewer pipes offers an
important application area for computer vision algorithms. This paper describes
a system, which is capable of accomplishing this task solely based on low
quality and severely compressed fisheye images from a pipe inspection robot.
Relying on robust image features, we estimate camera poses, model the image
lighting, and exploit this information to generate high quality cylindrical
unwraps of the pipes' surfaces.Based on the generated images, we apply semantic
labeling based on deep convolutional neural networks to detect and classify
defects as well as structural elements.
