OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic
  Segmentation of Underground Utilities
Lasse H. Hansen Simon B. Jensen Mark P. Philipsen Andreas Møgelmose Lars Bodum Thomas B. Moeslund
Identifying and classifying underground utilities is an important task for
efficient and effective urban planning and infrastructure maintenance. We
present OpenTrench3D, a novel and comprehensive 3D Semantic Segmentation point
cloud dataset, designed to advance research and development in underground
utility surveying and mapping. OpenTrench3D covers a completely novel domain
for public 3D point cloud datasets and is unique in its focus, scope, and
cost-effective capturing method. The dataset consists of 310 point clouds
collected across 7 distinct areas. These include 5 water utility areas and 2
district heating utility areas. The inclusion of different geographical areas
and main utilities (water and district heating utilities) makes OpenTrench3D
particularly valuable for inter-domain transfer learning experiments. We
provide benchmark results for the dataset using three state-of-the-art semantic
segmentation models, PointNeXt, PointVector and PointMetaBase. Benchmarks are
conducted by training on data from water areas, fine-tuning on district heating
area 1 and evaluating on district heating area 2. The dataset is publicly
available. With OpenTrench3D, we seek to foster innovation and progress in the
field of 3D semantic segmentation in applications related to detection and
documentation of underground utilities as well as in transfer learning methods
in general.