# SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving

arXiv: https://arxiv.org/abs/2203.05056 · HTML: https://mlsanity.com/paper/2203.05056/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 46 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 22 of 22)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?](https://mlsanity.com/paper/1610.01983/) (2016)
- [The ApolloScape Open Dataset for Autonomous Driving and its Application](https://mlsanity.com/paper/1803.06184/) (2018)
- [Playing for Benchmarks](https://mlsanity.com/paper/1709.07322/) (2017)
- [WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving](https://mlsanity.com/paper/1905.01489/) (2019)
- [OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving](https://mlsanity.com/paper/2102.07448/) (2021)
- [SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2008.04017/) (2020)
- [Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline](https://mlsanity.com/paper/2012.02124/) (2020)
- [FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving](https://mlsanity.com/paper/1910.04076/) (2019)
- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models](https://mlsanity.com/paper/2007.06676/) (2020)
- [SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras](https://mlsanity.com/paper/2104.04420/) (2021)
- [Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras](https://mlsanity.com/paper/2103.17001/) (2021)
- [Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning](https://mlsanity.com/paper/2104.14042/) (2021)
- [Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision](https://mlsanity.com/paper/1803.06192/) (2018)
- [FisheyeMODNet: Moving Object detection on Surround-view Cameras for Autonomous Driving](https://mlsanity.com/paper/1908.11789/) (2019)
- [TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric](https://mlsanity.com/paper/2007.00801/) (2020)
- [Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving](https://mlsanity.com/paper/2107.07449/) (2021)
- [An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras](https://mlsanity.com/paper/2105.12763/) (2021)
- [A Hybrid Sparse-Dense Monocular SLAM System for Autonomous Driving](https://mlsanity.com/paper/2108.07736/) (2021)
- [Capsule Neural Network based Height Classification using Low-Cost Automotive Ultrasonic Sensors](https://mlsanity.com/paper/1902.09839/) (2019)

## Built on by (most cited first, 30 of 32)

- [Delivering Arbitrary-Modal Semantic Segmentation](https://mlsanity.com/paper/2303.01480/) (2023)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation](https://mlsanity.com/paper/2207.11860/) (2022)
- [Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges](https://mlsanity.com/paper/2205.13281/) (2022)
- [SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints](https://mlsanity.com/paper/2204.09788/) (2022)
- [HEAL-SWIN: A Vision Transformer On The Sphere](https://mlsanity.com/paper/2307.07313/) (2023)
- [AmodalSynthDrive: A Synthetic Amodal Perception Dataset for Autonomous Driving](https://mlsanity.com/paper/2309.06547/) (2023)
- [F2BEV: Bird's Eye View Generation from Surround-View Fisheye Camera Images for Automated Driving](https://mlsanity.com/paper/2303.03651/) (2023)
- [Optical Flow for Autonomous Driving: Applications, Challenges and Improvements](https://mlsanity.com/paper/2301.04422/) (2023)
- [3DGEER: 3D Gaussian Rendering Made Exact and Efficient for Generic Cameras](https://mlsanity.com/paper/2505.24053/) (2025)
- [Deformable Mamba for Wide Field of View Segmentation](https://mlsanity.com/paper/2411.16481/) (2024)
- [Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving](https://mlsanity.com/paper/2503.00675/) (2025)
- [ADD: An Automatic Desensitization Fisheye Dataset for Autonomous Driving](https://mlsanity.com/paper/2308.07590/) (2023)
- [Woodscape Fisheye Object Detection for Autonomous Driving -- CVPR 2022 OmniCV Workshop Challenge](https://mlsanity.com/paper/2206.12912/) (2022)
- [IBISCape: A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments](https://mlsanity.com/paper/2206.13455/) (2022)
- [Applications of Deep Learning for Top-View Omnidirectional Imaging: A Survey](https://mlsanity.com/paper/2304.08193/) (2023)
- [Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges](https://mlsanity.com/paper/2402.12041/) (2024)
- [RealDriveSim: A Realistic Multi-Modal Multi-Task Synthetic Dataset for Autonomous Driving](https://mlsanity.com/paper/2506.16319/) (2025)
- [FishBEV: Distortion-Resilient Bird's Eye View Segmentation with Surround-View Fisheye Cameras](https://mlsanity.com/paper/2509.13681/) (2025)
- [Rethinking Camera Choice: An Empirical Study on Fisheye Camera Properties in Robotic Manipulation](https://mlsanity.com/paper/2603.02139/) (2026)
- [Surround-View Cameras based Holistic Visual Perception for Automated Driving](https://mlsanity.com/paper/2206.05542/) (2022)
- [Sector Patch Embedding: An Embedding Module Conforming to The Distortion Pattern of Fisheye Image](https://mlsanity.com/paper/2303.14645/) (2023)
- [DaF-BEVSeg: Distortion-aware Fisheye Camera based Bird's Eye View Segmentation with Occlusion Reasoning](https://mlsanity.com/paper/2404.06352/) (2024)
- [Deformable Convolution Based Road Scene Semantic Segmentation of Fisheye Images in Autonomous Driving](https://mlsanity.com/paper/2407.16647/) (2024)
- [FlyAwareV2: A Multimodal Cross-Domain UAV Dataset for Urban Scene Understanding](https://mlsanity.com/paper/2510.13243/) (2025)
- [Benchmarking Multi-View BEV Object Detection with Mixed Pinhole and Fisheye Cameras](https://mlsanity.com/paper/2603.27818/) (2026)
- [SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving](https://mlsanity.com/paper/2206.02876/) (2022)
- [Measuring Natural Scenes SFR of Automotive Fisheye Cameras](https://mlsanity.com/paper/2401.05232/) (2024)
- [Training for X-Ray Vision: Amodal Segmentation, Amodal Content Completion, and View-Invariant Object Representation from Multi-Camera Video](https://mlsanity.com/paper/2507.00339/) (2025)
- [WideDepth: Millimeter-Accurate Benchmark for Fisheye Depth Estimation](https://mlsanity.com/paper/2605.24074/) (2026)

## Abstract (arXiv)

Surround-view cameras are a primary sensor for automated driving, used for near-field perception. It is one of the most commonly used sensors in commercial vehicles primarily used for parking visualization and automated parking. Four fisheye cameras with a 190deg field of view cover the 360deg around the vehicle. Due to its high radial distortion, the standard algorithms do not extend easily. Previously, we released the first public fisheye surround-view dataset named WoodScape. In this work, we release a synthetic version of the surround-view dataset, covering many of its weaknesses and extending it. Firstly, it is not possible to obtain ground truth for pixel-wise optical flow and depth. Secondly, WoodScape did not have all four cameras annotated simultaneously in order to sample diverse frames. However, this means that multi-camera algorithms cannot be designed to obtain a unified output in birds-eye space, which is enabled in the new dataset. We implemented surround-view fisheye geometric projections in CARLA Simulator matching WoodScape's configuration and created SynWoodScape. We release 80k images from the synthetic dataset with annotations for 10+ tasks. We also release the baseline code and supporting scripts.