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Virtual KITTI 3D Dataset for Semantic Segmentation


This is the outdoor dataset used to evaluate 3D semantic segmentation of point clouds in (Engelmann et al. ICCV'W17) Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds paper. The dataset is directly derived from the Virtual KITTI Dataset (v.1.3.1).

For the above paper, version 1 was used.


All files can be generated with the provided scripts in this repository. However, for convenience only, we provide all files on our server for downloading.

Generating the dataset

./ path/to/root/directory
  • Create numpy files from raw data files. (For additional information about the parameter please refer to the provided argparse manual)
python --root_path path/to/root/vkitti_raw --out_path path/to/root/vkitti_npy --sequence 0001 --v2
python --root_path path/to/root/vkitti_npy --out_path path/to/root/vkitti_sets

Data Format

All files are provided as numpy .npy files. Each file contains a N x F matrix, where N is the number of points in a scene and F is the number of features per point, in this case F=7. The features are XYZRGBL, the 3D XYZ position, the RGB color and the ground truth semantic label L. You can load them as follows:

import numpy as np
point_cloud = np.load('dataset/01/0001_00000.npy')  # shape: (401326, 7)
Label ID Semantics RGB Color
0 Terrain [200, 90, 0] brown
1 Tree [0, 128, 50] dark green
2 Vegetation [0, 220, 0] bright green
3 Building [255, 0, 0] red
4 Road [100, 100, 100] dark gray
5 GuardRail [200, 200, 200] bright gray
6 TrafficSign [255, 0, 255] pink
7 TrafficLight [255, 255, 0] yellow
8 Pole [128, 0, 255] violet
9 Misc [255, 200, 150] skin
10 Truck [0, 128, 255] dark blue
11 Car [0, 200, 255] bright blue
12 Van [255, 128, 0] orange
13 Don't care [0, 0, 0] black


If you use this dataset in your paper, please consider citing our ICCV'17 workshop paper:

  author    = {Francis, Engelmann and Theodora, Kontogianni and Alexander, Hermans and Bastian, Leibe},
  title     = {Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds},
  booktitle = {{IEEE} International Conference on Computer Vision, 3DRMS Workshop, {ICCV}},
  year      = {2017}

This dataset is based on:

    author = {Gaidon, A and Wang, Q and Cabon, Y and Vig, E},
    title = {Virtual Worlds as Proxy for Multi-Object Tracking Analysis},
    booktitle = {CVPR},
    year = {2016}

Training and Evaluation

We trained our models using 6-fold cross validation as advertised in Qi et al. (PointNet). For example, you train your model using sequences 2-5 and evaluate on 1. You then do this for all six sequences and average your numbers of all six splits. We report the mean intersection over union (IoU), overall accuracy (over all points) and the average class accuracy.


To visualize the point clouds:

  • Install anaconda or miniconda
  • conda create --name vkitti3d python=3.6
  • source activate vkitti3d
  • conda install numpy
  • conda install -c clinicalgraphics vtk

After installation you can visualize the original RGB point cloud and the corresponding semantic labels:

cd tools python

Dataset Generation

How was this dataset generated? We split up the original sequences of Virtual KITTI into non-overlapping sub-sequences to perform 6-fold cross validation. For each sub-sequence, we selected 15 scenes/frames at equidistant timesteps to avoid overlapping data.

Original Ours Train/Test Frames - VKITTI
1 1 0 - 170: 0,12,24,36,48,60,72,85,97,109,121,133,145,157, 170
1 2 230 - 420: 230,243,257,270,284,297,311,325,338,352,365,379,392,406,420
2 3 0 - 232: 0,15,31,47,63,79,95,111,127,143,159,175,191,207,223
18 4 30 - 338 30,52,74,96,118,140,162,184,206,228,250,272,294,316,338
20 5 80 - 444: 80,106,132,158,184,210,236,262,288,314,340,366,392,418,444
20 6 500 - 800: 500,521,542,564,585,607,628,650,671,692,714,735,757,778,800

Additional information about version 2

Fix of the projection error occuring in the VKITTI numpy pointclouds of version 1 introduced by the wrong depth of car windows. The problem with the original point clouds is that they are created from RGBD images. Voxels behind car glass will get the car class label and thus, decreasing the quality of the test and training set.

We introduced a new semantic class (don't care class). Voxels wrongly labeled with the car class will be assigned to this class.

In order to further increase the quality of our ground truth, we projected all images into world space and interpolated points with don't care class labels if labeled points where close by to these points.

The above figure shows the difference between the original pointcloud and the fixed version (black color illustrates the don't care class).