SemanticSpray Dataset

LiDARs are one of the main sensors used for autonomous driving applications, providing accurate depth estimation regardless of lighting conditions. However, they are severely affected by adverse weather conditions such as rain, snow, and fog.

This dataset provides semantic labels for a subset of the Road Spray Dataset, which contains scenes of vehicles traveling at different speeds on wet surfaces, creating a trailing spray effect. We provide semantic labels for over 200 dynamic scenes, labeling each point in the LiDAR point clouds as background (road, vegetation, buildings, ...), foreground (moving vehicles), and noise (spray, LiDAR artifacts).

• Download the dataset from here. Afterwards, you should have the following files:
  • SemanticSprayDataset.zip
  • SemanticSprayDataset.z01
  • SemanticSprayDataset.z02
  • SemanticSprayDataset.z03
  • SemanticSprayDataset.z04
  • SemanticSprayDataset.z05
• Combine all of the zip files in one single file:
  $ zip -F SemanticSprayDataset.zip --out SemanticSprayDataset_single_file.zip
• Extract the files:
  $ unzip SemanticSprayDataset_single_file.zip
• The extracted dataset should have a structure like this:
  

  
  |--- Crafter_dynamic
  |   |--- 0000_2021-09-08-14-36-56_0
  |   |   |--- image_2
  |   |   |   |--- 000000.jpg
  |   |   |   |--- ....
  |   |   |--- delphi_radar
  |   |   |   |--- 000000.bin
  |   |   |   |--- ....
  |   |   |--- ibeo_front
  |   |   |   |--- 000000.bin
  |   |   |   |--- ....
  |   |   |--- ibeo_rear
  |   |   |   |--- 000000.bin
  |   |   |   |--- ....
  |   |   |--- labels
  |   |   |   |--- 000000.label
  |   |   |   |--- ....
  |   |   |--- velodyne
  |   |   |   |--- 000000.bin
  |   |   |   |--- ....
  |   |   |--- poses.txt
  |   |   |--- metadata.txt
  |--- Golf_dynamic
  ...
  

The sensor setup used for the recordings is the following:

• 1 Front Camera
• 1 Velodyne VLP32C LiDAR (top mounted high-resolution LiDAR)
• 2 Ibeo LUX 2010 LiDAR (front and rear mounted, low-resolution LiDAR)
• 1 Aptiv ESR 2.5 Radar

• [Camera Image] in the folder "image_2"
• [VLP32C LiDAR] in the folder "velodyne"
• [VLIbeo LUX 2010 LiDAR front] in the folder "ibeo_front"
• [VLIbeo LUX 2010 LiDAR rear] in the folder "ibeo_rear"
• [Aptiv ESR 2.5 Radar] in the folder "delphi_radar"
• [Semantic Labels for VLP32C LiDAR] in the folder "labels"
• The ego vehicle poses are located in the file "poses.txt". The convention used by the SemanticKITTI dataset is followed.
• Additional information on the scene setup (e.g., ego_velocity) are given in the "metadata.txt" file.

• Background: 0
• Leading Vehicle: 1
• Spray and other noise artifacts : 2

In the following GitHub repository, we provide python code for loading and visualizing the dataset.