mlsanityData to 14 Oct 2026

Spatiotemporal Deformation Perception for Fisheye Video Rectification

arXiv 2302.03934 ↗First posted 8 Feb 2023cs.CVcited by 2

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Abstract (arXiv)

Although the distortion correction of fisheye images has been extensively studied, the correction of fisheye videos is still an elusive challenge. For different frames of the fisheye video, the existing image correction methods ignore the correlation of sequences, resulting in temporal jitter in the corrected video. To solve this problem, we propose a temporal weighting scheme to get a plausible global optical flow, which mitigates the jitter effect by progressively reducing the weight of frames. Subsequently, we observe that the inter-frame optical flow of the video is facilitated to perceive the local spatial deformation of the fisheye video. Therefore, we derive the spatial deformation through the flows of fisheye and distorted-free videos, thereby enhancing the local accuracy of the predicted result. However, the independent correction for each frame disrupts the temporal correlation. Due to the property of fisheye video, a distorted moving object may be able to find its distorted-free pattern at another moment. To this end, a temporal deformation aggregator is designed to reconstruct the deformation correlation between frames and provide a reliable global feature. Our method achieves an end-to-end correction and demonstrates superiority in correction quality and stability compared with the SOTA correction methods.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 42 references that are in the map. First posted on arXiv 8 Feb 2023.

Builds on

15 of 42 references.

References · 15

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  2. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
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  6. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,415
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  8. 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,457
  9. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 915
  10. 2018YouTube-VOS: Sequence-to-Sequence Video Object Segmentationcited by 232
  11. 2019Target-Aware Deep Trackingcited by 43
  12. 2018FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectificationcited by 32
3 more
  1. 2019Blind Geometric Distortion Correction on Images Through Deep Learningcited by 28
  2. 2019Learning to Calibrate Straight Lines for Fisheye Image Rectificationcited by 23
  3. 2021Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flowcited by 23

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Citing papers per year: 2023: 2.

2 papers.