mlsanityData to 14 Oct 2026

Woodscape Fisheye Object Detection for Autonomous Driving -- CVPR 2022 OmniCV Workshop Challenge

arXiv 2206.12912 ↗First posted 26 Jun 2022cs.CVcited by 3

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Abstract (arXiv)

Object detection is a comprehensively studied problem in autonomous driving. However, it has been relatively less explored in the case of fisheye cameras. The strong radial distortion breaks the translation invariance inductive bias of Convolutional Neural Networks. Thus, we present the WoodScape fisheye object detection challenge for autonomous driving which was held as part of the CVPR 2022 Workshop on Omnidirectional Computer Vision (OmniCV). This is one of the first competitions focused on fisheye camera object detection. We encouraged the participants to design models which work natively on fisheye images without rectification. We used CodaLab to host the competition based on the publicly available WoodScape fisheye dataset. In this paper, we provide a detailed analysis on the competition which attracted the participation of 120 global teams and a total of 1492 submissions. We briefly discuss the details of the winning methods and analyze their qualitative and quantitative results.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 39 of its 54 references that are in the map. First posted on arXiv 26 Jun 2022.

Builds on

39 of 54 references.

References · 39

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27 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 1.

3 papers.

  1. 2023A Comprehensive Overview of Fish-Eye Camera Distortion Correction Methodscited by 3
  2. 2023WoodScape Motion Segmentation for Autonomous Driving -- CVPR 2023 OmniCV Workshop Challengecited by 0
  3. 2026FUSEye: Training-Light Fisheye Detection with Overlapping Views and Zero-Initialized Adapterscited by 0