Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios

Tobias Gruber, Mario Bijelic, Felix Heide, Werner Ritter, Klaus Dietmayer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations


This work introduces an evaluation benchmark for depth estimation and completion using high-resolution depth measurements with angular resolution of up to 25' (arcsecond), akin to a 50 megapixel camera with per-pixel depth available. Existing datasets, such as the KITTI benchmark, provide only sparse reference measurements with an order of magnitude lower angular resolution-these sparse measurements are treated as ground truth by existing depth estimation methods. We propose an evaluation methodology in four characteristic automotive scenarios recorded in varying weather conditions (day, night, fog, rain). As a result, our benchmark allows us to evaluate the robustness of depth sensing methods in adverse weather and different driving conditions. Using the proposed evaluation data, we demonstrate that current stereo approaches provide significantly more stable depth estimates than monocular methods and lidar completion in adverse weather. Data and code are available at https://github.com/gruberto/PixelAccurateDepthBenchmark.git.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 International Conference on 3D Vision, 3DV 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages11
ISBN (Electronic)9781728131313
StatePublished - Sep 2019
Event7th International Conference on 3D Vision, 3DV 2019 - Quebec, Canada
Duration: Sep 15 2019Sep 18 2019

Publication series

NameProceedings - 2019 International Conference on 3D Vision, 3DV 2019


Conference7th International Conference on 3D Vision, 3DV 2019

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Media Technology
  • Modeling and Simulation


  • 3D perception
  • benchmark
  • depth estimation
  • self driving cars


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