Sewer Defect Classification using Synthetic Point Clouds

Joakim Bruslund Haurum, Moaaz Mohamed Jamal Allahham, Mathias Stougaard Lynge, Kasper Schøn Henriksen, Ivan Adriyanov Nikolov, Thomas B. Moeslund

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

6 Citationer (Scopus)


Sewer pipes are currently manually inspected by trained inspectors, making the process prone to human errors, which can be potentially critical. There is therefore a great research and industry interest in automating the sewer inspection process. Previous research have been focused on working with 2D image data, similar to how inspections are currently conducted. There is, however, a clear potential for utilizing recent advances within 3D computer vision for this task. In this paper we investigate the feasibility of applying two modern deep learning methods, DGCNN and PointNet, on a new publicly available sewer point cloud dataset. As point cloud data from real sewers is scarce, we investigate using synthetic data to bootstrap the training process. We investigate four data scenarios, and find that training on synthetic data and fine-tune on real data gives the best results, increasing the metrics by 6-10 percentage points for the best model. Data and code is available at
TitelProceedings of the 16th International Conference on Computer Vision Theory and Applications (VISAPP)
RedaktørerGiovanni Maria Farinella, Petia Radeva, Jose Braz, Kadi Bouatouch
Antal sider10
ForlagSCITEPRESS Digital Library
ISBN (Elektronisk)9789897584886
StatusUdgivet - 2021
BegivenhedInternational Conference on Computer Vision Theory and Applications -
Varighed: 8 feb. 202110 feb. 2021
Konferencens nummer: 16


KonferenceInternational Conference on Computer Vision Theory and Applications


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