Core Concepts
OpenTrench3D is a novel and comprehensive 3D Semantic Segmentation point cloud dataset designed to advance research and development in underground utility surveying and mapping.
Abstract
The OpenTrench3D dataset consists of 310 point clouds collected across 7 distinct areas, including 5 water utility areas and 2 district heating utility areas. The dataset follows a utility owner-centric classification scheme with 5 classes: Trench, Main Utility, Other Utility, Inactive Utility, and Misc.
The authors provide benchmark results using three state-of-the-art semantic segmentation models: PointNeXt, PointVector, and PointMetaBase. The 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 results highlight the potential and effectiveness of transfer learning across utility types and evaluate the generalizability of SOTA methods.
The dataset is publicly available, and the authors aim to foster innovation and progress in the field of 3D semantic segmentation for applications related to detection and documentation of underground utilities, as well as in transfer learning methods in general.
Stats
The dataset consists of approximately 528 million points across 310 point clouds.
Quotes
"OpenTrench3D covers a completely novel domain for public 3D point cloud datasets and is unique in its focus, scope, and cost-effective capturing method."
"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."