Published February 21, 2025 | Version v3
Dataset Open

SemanticTHAB: A High Resolution LiDAR Dataset

Creators

  • 1. ROR icon Aschaffenburg University of Applied Sciences
  • 2. ROR icon University of Kassel

Contributors

Project leader:

  • 1. ROR icon Aschaffenburg University of Applied Sciences
  • 2. ROR icon University of Kassel

Description

The SemanticTHAB dataset is a large-scale dataset designed for semantic segmentation in autonomous driving. It contains 4,750 3D LiDAR point clouds collected from urban environments. The dataset includes labeled point clouds with 20 semantic classes, such as road, car, pedestrian, and building. It provides ground truth annotations for training and evaluating semantic segmentation algorithms, offering a real-world benchmark for 3D scene understanding in self-driving car applications. The dataset is desinged to extent the SemanticKITTI benchmark by scans of a modern high resolution LiDAR sensor (Ouster OS2-128, Rev7).

Files

SemanticTHAB.zip

Files (14.3 GB)

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Additional details

Related works

References
Publication: https://www.semantic-kitti.org/ (URL)

Dates

Submitted
2025-01-17

Software

Repository URL
https://github.com/kav-institute/SemanticLiDAR
Programming language
Python, Dockerfile
Development Status
Concept