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KITTI-depth
Depth
Autonomous Driving
|...
License: CC BY-NC-SA 3.0

Overview

The depth completion and depth prediction evaluation are related to our work published in Sparsity
Invariant CNNs (THREEDV 2017)
. KITTI-depth
contains over 93 thousand depth maps with corresponding raw LiDaR scans and RGB images. Given
the large amount of training data, this dataset shall allow a training of complex deep learning
models for the tasks of depth completion and single image depth prediction. Also, we provide
manually selected images with unpublished depth maps to serve as a benchmark for those two
challenging tasks.

Data Collection

We equipped a standard station wagon with two high-resolution color and grayscale video cameras.
Accurate ground truth is provided by a Velodyne laser scanner and a GPS localization system.
Our datsets are captured by driving around the mid-size city of Karlsruhe, in rural areas and
on highways. Up to 15 cars and 30 pedestrians are visible per image.

img

Citation

Please use the following citation when referencing the dataset:

@INPROCEEDINGS{Uhrig2017THREEDV,
  author = {Jonas Uhrig and Nick Schneider and Lukas Schneider and Uwe Franke and Thomas Brox
and Andreas Geiger},
  title = {Sparsity Invariant CNNs},
  booktitle = {International Conference on 3D Vision (3DV)},
  year = {2017}
}

License

CC BY-NC-SA 3.0

Data Summary
Type
Image,
Amount
93K
Size
19.95GB
Provided by
Max Planck Institute for Intellgent Systems
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems
| Amount 93K | Size 19.95GB
KITTI-depth
Depth
Autonomous Driving
License: CC BY-NC-SA 3.0

Overview

The depth completion and depth prediction evaluation are related to our work published in Sparsity
Invariant CNNs (THREEDV 2017)
. KITTI-depth
contains over 93 thousand depth maps with corresponding raw LiDaR scans and RGB images. Given
the large amount of training data, this dataset shall allow a training of complex deep learning
models for the tasks of depth completion and single image depth prediction. Also, we provide
manually selected images with unpublished depth maps to serve as a benchmark for those two
challenging tasks.

Data Collection

We equipped a standard station wagon with two high-resolution color and grayscale video cameras.
Accurate ground truth is provided by a Velodyne laser scanner and a GPS localization system.
Our datsets are captured by driving around the mid-size city of Karlsruhe, in rural areas and
on highways. Up to 15 cars and 30 pedestrians are visible per image.

img

Citation

Please use the following citation when referencing the dataset:

@INPROCEEDINGS{Uhrig2017THREEDV,
  author = {Jonas Uhrig and Nick Schneider and Lukas Schneider and Uwe Franke and Thomas Brox
and Andreas Geiger},
  title = {Sparsity Invariant CNNs},
  booktitle = {International Conference on 3D Vision (3DV)},
  year = {2017}
}

License

CC BY-NC-SA 3.0

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