mlsanityData to 7 Oct 2026

Detecting Road Obstacles by Erasing Them

arXiv 2012.13633 ↗First posted 25 Dec 2020cs.CVcited by 29

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Abstract (arXiv)

Vehicles can encounter a myriad of obstacles on the road, and it is impossible to record them all beforehand to train a detector. Instead, we select image patches and inpaint them with the surrounding road texture, which tends to remove obstacles from those patches. We then use a network trained to recognize discrepancies between the original patch and the inpainted one, which signals an erased obstacle.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Dec 2020.

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Citing papers per year: 2021: 1; 2022: 5; 2023: 7; 2024: 7; 2025: 7; 2026: 2.

29 papers.

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  1. 2022RbA: Segmenting Unknown Regions Rejected by Allcited by 67
  2. 2022DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognitioncited by 42
  3. 2023On Advantages of Mask-level Recognition for Outlier-aware Segmentationcited by 42
  4. 2024Open-World Semantic Segmentation Including Class Similaritycited by 22
  5. 2024PixOOD: Pixel-Level Out-of-Distribution Detectioncited by 20
  6. 2022Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objectscited by 19
  7. 2024MirrorCheck: Efficient Adversarial Defense for Vision-Language Modelscited by 19
  8. 2023UGainS: Uncertainty Guided Anomaly Instance Segmentationcited by 15
  9. 2023CSL: Class-Agnostic Structure-Constrained Learning for Segmentation Including the Unseencited by 15
  10. 2024Placing Objects in Context via Inpainting for Out-of-distribution Segmentationcited by 12
  11. 2023Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentationcited by 11
  12. 2021Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Datacited by 10
17 more
  1. 2023Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenescited by 9
  2. 2023Hybrid Open-set Segmentation with Synthetic Negative Datacited by 8
  3. 2025Segmenting Objectiveness and Task-awareness Unknown Region for Autonomous Drivingcited by 5
  4. 2022Detecting and Learning the Unknown in Semantic Segmentationcited by 4
  5. 2024VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detectioncited by 4
  6. 2024Segment-Level Road Obstacle Detection Using Visual Foundation Model Priors and Likelihood Ratioscited by 3
  7. 2024Multi-Scale Foreground-Background Confidence for Out-of-Distribution Segmentationcited by 3
  8. 2025Uncertainty-Aware Likelihood Ratio Estimation for Pixel-Wise Out-of-Distribution Detectioncited by 3
  9. 2022Perspective Aware Road Obstacle Detectioncited by 2
  10. 2023A Metacognitive Approach to Out-of-Distribution Detection for Segmentationcited by 2
  11. 2025OoDDINO:A Multi-level Framework for Anomaly Segmentation on Complex Road Scenescited by 2
  12. 2025Stereo-based 3D Anomaly Object Detection for Autonomous Driving: A New Dataset and Baselinecited by 2
  13. 2025What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studiescited by 1
  14. 2025Panoramic Out-of-Distribution Segmentationcited by 0
  15. 2025ClimaOoD: Improving Anomaly Segmentation via Physically Realistic Synthetic Datacited by 0
  16. 2026Trinity: Unifying Class-Agnostic Terrain and Semantic Segmentation for Unstructured Outdoor Environments by Leveraging Synthetic Datacited by 0
  17. 2026Combining Foundation Model Confidence and Monocular Depth for Training-Free Out-of-Distribution Segmentationcited by 0