---
_id: '33451'
abstract:
- lang: eng
  text: "We present an approach to automatically generate semantic labels for real
    recordings of automotive range-Doppler (RD) radar spectra. Such labels are required
    when training a neural network for object recognition from radar data. The automatic
    labeling approach rests on the simultaneous recording of camera and lidar data
    in addition to the radar spectrum. By warping radar spectra into the camera image,
    state-of-the-art object recognition algorithms can be applied to label relevant
    objects, such as cars, in the camera image. The warping operation is designed
    to be fully differentiable, which allows backpropagating the gradient computed
    on the camera image through the warping operation to the neural network operating
    on the radar data. As the warping operation relies on accurate scene flow estimation,
    we further propose a novel scene flow estimation algorithm which exploits information
    from camera, lidar and radar sensors. The\r\nproposed scene flow estimation approach
    is compared against a state-of-the-art scene flow algorithm, and it outperforms
    it by approximately 30% w.r.t. mean average error. The feasibility of the overall
    framework for automatic label generation for\r\nRD spectra is verified by evaluating
    the performance of neural networks trained with the proposed framework for Direction-of-Arrival
    estimation."
author:
- first_name: Christopher
  full_name: Grimm, Christopher
  last_name: Grimm
- first_name: Tai
  full_name: Fei, Tai
  last_name: Fei
- first_name: Ernst
  full_name: Warsitz, Ernst
  last_name: Warsitz
- first_name: Ridha
  full_name: Farhoud, Ridha
  last_name: Farhoud
- first_name: Tobias
  full_name: Breddermann, Tobias
  last_name: Breddermann
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: Grimm C, Fei T, Warsitz E, Farhoud R, Breddermann T, Haeb-Umbach R. Warping
    of Radar Data Into Camera Image for Cross-Modal Supervision in Automotive Applications.
    <i>IEEE Transactions on Vehicular Technology</i>. 2022;71(9):9435-9449. doi:<a
    href="https://doi.org/10.1109/TVT.2022.3182411">10.1109/TVT.2022.3182411</a>
  apa: Grimm, C., Fei, T., Warsitz, E., Farhoud, R., Breddermann, T., &#38; Haeb-Umbach,
    R. (2022). Warping of Radar Data Into Camera Image for Cross-Modal Supervision
    in Automotive Applications. <i>IEEE Transactions on Vehicular Technology</i>,
    <i>71</i>(9), 9435–9449. <a href="https://doi.org/10.1109/TVT.2022.3182411">https://doi.org/10.1109/TVT.2022.3182411</a>
  bibtex: '@article{Grimm_Fei_Warsitz_Farhoud_Breddermann_Haeb-Umbach_2022, title={Warping
    of Radar Data Into Camera Image for Cross-Modal Supervision in Automotive Applications},
    volume={71}, DOI={<a href="https://doi.org/10.1109/TVT.2022.3182411">10.1109/TVT.2022.3182411</a>},
    number={9}, journal={IEEE Transactions on Vehicular Technology}, author={Grimm,
    Christopher and Fei, Tai and Warsitz, Ernst and Farhoud, Ridha and Breddermann,
    Tobias and Haeb-Umbach, Reinhold}, year={2022}, pages={9435–9449} }'
  chicago: 'Grimm, Christopher, Tai Fei, Ernst Warsitz, Ridha Farhoud, Tobias Breddermann,
    and Reinhold Haeb-Umbach. “Warping of Radar Data Into Camera Image for Cross-Modal
    Supervision in Automotive Applications.” <i>IEEE Transactions on Vehicular Technology</i>
    71, no. 9 (2022): 9435–49. <a href="https://doi.org/10.1109/TVT.2022.3182411">https://doi.org/10.1109/TVT.2022.3182411</a>.'
  ieee: 'C. Grimm, T. Fei, E. Warsitz, R. Farhoud, T. Breddermann, and R. Haeb-Umbach,
    “Warping of Radar Data Into Camera Image for Cross-Modal Supervision in Automotive
    Applications,” <i>IEEE Transactions on Vehicular Technology</i>, vol. 71, no.
    9, pp. 9435–9449, 2022, doi: <a href="https://doi.org/10.1109/TVT.2022.3182411">10.1109/TVT.2022.3182411</a>.'
  mla: Grimm, Christopher, et al. “Warping of Radar Data Into Camera Image for Cross-Modal
    Supervision in Automotive Applications.” <i>IEEE Transactions on Vehicular Technology</i>,
    vol. 71, no. 9, 2022, pp. 9435–49, doi:<a href="https://doi.org/10.1109/TVT.2022.3182411">10.1109/TVT.2022.3182411</a>.
  short: C. Grimm, T. Fei, E. Warsitz, R. Farhoud, T. Breddermann, R. Haeb-Umbach,
    IEEE Transactions on Vehicular Technology 71 (2022) 9435–9449.
date_created: 2022-09-21T07:26:19Z
date_updated: 2023-11-20T16:37:16Z
ddc:
- '000'
department:
- _id: '54'
doi: 10.1109/TVT.2022.3182411
file:
- access_level: open_access
  content_type: application/pdf
  creator: huesera
  date_created: 2022-09-22T07:00:29Z
  date_updated: 2022-09-22T07:00:29Z
  file_id: '33460'
  file_name: T-VT_AcceptedVersion.pdf
  file_size: 12117870
  relation: main_file
file_date_updated: 2022-09-22T07:00:29Z
has_accepted_license: '1'
intvolume: '        71'
issue: '9'
language:
- iso: eng
oa: '1'
page: 9435-9449
publication: IEEE Transactions on Vehicular Technology
quality_controlled: '1'
status: public
title: Warping of Radar Data Into Camera Image for Cross-Modal Supervision in Automotive
  Applications
type: journal_article
user_id: '242'
volume: 71
year: '2022'
...