WADS 3D Object Detection Dataset (WADS-3D) - sequence 77 lidar
Abstract
WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.
Fields of Study & Subjects
Autonomous Vehicle Technology and Safety
Advanced Neural Network Applications
Advanced Optical Sensing Technologies
Publication details
- Journal / source
- Digital Commons - Michigan Tech (Michigan Technological University)
- Publication date
- 5 February 2026