# Detecting Road Obstacles by Erasing Them

arXiv: https://arxiv.org/abs/2012.13633 · HTML: https://mlsanity.com/paper/2012.13633/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2020.

## Built on by (most cited first, 29 of 29)

- [RbA: Segmenting Unknown Regions Rejected by All](https://mlsanity.com/paper/2211.14293/) (2022)
- [DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition](https://mlsanity.com/paper/2207.02606/) (2022)
- [On Advantages of Mask-level Recognition for Outlier-aware Segmentation](https://mlsanity.com/paper/2301.03407/) (2023)
- [Open-World Semantic Segmentation Including Class Similarity](https://mlsanity.com/paper/2403.07532/) (2024)
- [PixOOD: Pixel-Level Out-of-Distribution Detection](https://mlsanity.com/paper/2405.19882/) (2024)
- [Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects](https://mlsanity.com/paper/2210.02074/) (2022)
- [MirrorCheck: Efficient Adversarial Defense for Vision-Language Models](https://mlsanity.com/paper/2406.09250/) (2024)
- [UGainS: Uncertainty Guided Anomaly Instance Segmentation](https://mlsanity.com/paper/2308.02046/) (2023)
- [CSL: Class-Agnostic Structure-Constrained Learning for Segmentation Including the Unseen](https://mlsanity.com/paper/2312.05538/) (2023)
- [Placing Objects in Context via Inpainting for Out-of-distribution Segmentation](https://mlsanity.com/paper/2402.16392/) (2024)
- [Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation](https://mlsanity.com/paper/2303.06920/) (2023)
- [Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data](https://mlsanity.com/paper/2112.12833/) (2021)
- [Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes](https://mlsanity.com/paper/2309.04302/) (2023)
- [Hybrid Open-set Segmentation with Synthetic Negative Data](https://mlsanity.com/paper/2301.08555/) (2023)
- [Segmenting Objectiveness and Task-awareness Unknown Region for Autonomous Driving](https://mlsanity.com/paper/2504.19183/) (2025)
- [Detecting and Learning the Unknown in Semantic Segmentation](https://mlsanity.com/paper/2202.08700/) (2022)
- [VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection](https://mlsanity.com/paper/2409.17330/) (2024)
- [Segment-Level Road Obstacle Detection Using Visual Foundation Model Priors and Likelihood Ratios](https://mlsanity.com/paper/2412.05707/) (2024)
- [Multi-Scale Foreground-Background Confidence for Out-of-Distribution Segmentation](https://mlsanity.com/paper/2412.16990/) (2024)
- [Uncertainty-Aware Likelihood Ratio Estimation for Pixel-Wise Out-of-Distribution Detection](https://mlsanity.com/paper/2508.00587/) (2025)
- [Perspective Aware Road Obstacle Detection](https://mlsanity.com/paper/2210.01779/) (2022)
- [A Metacognitive Approach to Out-of-Distribution Detection for Segmentation](https://mlsanity.com/paper/2311.07578/) (2023)
- [Panoramic Out-of-Distribution Segmentation](https://mlsanity.com/paper/2505.03539/) (2025)
- [OoDDINO:A Multi-level Framework for Anomaly Segmentation on Complex Road Scenes](https://mlsanity.com/paper/2507.01455/) (2025)
- [Stereo-based 3D Anomaly Object Detection for Autonomous Driving: A New Dataset and Baseline](https://mlsanity.com/paper/2507.09214/) (2025)
- [What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies](https://mlsanity.com/paper/2507.06513/) (2025)
- [ClimaOoD: Improving Anomaly Segmentation via Physically Realistic Synthetic Data](https://mlsanity.com/paper/2512.02686/) (2025)
- [Trinity: Unifying Class-Agnostic Terrain and Semantic Segmentation for Unstructured Outdoor Environments by Leveraging Synthetic Data](https://mlsanity.com/paper/2605.27644/) (2026)
- [Combining Foundation Model Confidence and Monocular Depth for Training-Free Out-of-Distribution Segmentation](https://mlsanity.com/paper/2609.22896/) (2026)

## 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.