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KITTI-depth
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33c7b6ab-8ce2-11eb-b816-506b4b419b4c
fc9292e·
Jun 27, 2021 5:25 PM
·1Commits

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 Also, we provide manually selected images with unpublished depth maps to serve as a benchmark for those two challenging tasks.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.

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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}
}
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数据集信息
应用场景暂无
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LicenseCC BY-NC-SA 3.0
更新时间2020-12-31 17:27:44
数据概要
数据格式暂无
数据数量93K
已标注数量0
文件大小20MB
版权归属方
Max Planck Institute for Intellgent Systems
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