# ORFD: A Dataset and Benchmark for Off-Road Freespace Detection

arXiv: https://arxiv.org/abs/2206.09907 · HTML: https://mlsanity.com/paper/2206.09907/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 20 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020)
- [SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection](https://mlsanity.com/paper/2008.11351/) (2020)
- [Progressive LiDAR Adaptation for Road Detection](https://mlsanity.com/paper/1904.01206/) (2019)
- [Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator](https://mlsanity.com/paper/2005.08165/) (2020)

## Built on by (most cited first, 30 of 33)

- [The GOOSE Dataset for Perception in Unstructured Environments](https://mlsanity.com/paper/2310.16788/) (2023)
- [RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing](https://mlsanity.com/paper/2309.10356/) (2023)
- [WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments](https://mlsanity.com/paper/2312.15364/) (2023)
- [S3M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving](https://mlsanity.com/paper/2401.11414/) (2024)
- [Excavating in the Wild: The GOOSE-Ex Dataset for Semantic Segmentation](https://mlsanity.com/paper/2409.18788/) (2024)
- [RoadFormer+: Delivering RGB-X Scene Parsing through Scale-Aware Information Decoupling and Advanced Heterogeneous Feature Fusion](https://mlsanity.com/paper/2407.21631/) (2024)
- [WildOcc: A Benchmark for Off-Road 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2410.15792/) (2024)
- [SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection](https://mlsanity.com/paper/2402.18918/) (2024)
- [LIX: Implicitly Infusing Spatial Geometric Prior Knowledge into Visual Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2403.08215/) (2024)
- [Advancing Off-Road Autonomous Driving: The Large-Scale ORAD-3D Dataset and Comprehensive Benchmarks](https://mlsanity.com/paper/2510.16500/) (2025)
- [Autonomous Driving in Unstructured Environments: How Far Have We Come?](https://mlsanity.com/paper/2410.07701/) (2024)
- [ROD: RGB-Only Fast and Efficient Off-road Freespace Detection](https://mlsanity.com/paper/2508.08697/) (2025)
- [Adaptive-Mask Fusion Network for Segmentation of Drivable Road and Negative Obstacle With Untrustworthy Features](https://mlsanity.com/paper/2304.13979/) (2023)
- [Survey on Datasets for Perception in Unstructured Outdoor Environments](https://mlsanity.com/paper/2404.18750/) (2024)
- [TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework](https://mlsanity.com/paper/2407.18038/) (2024)
- [URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments](https://mlsanity.com/paper/2309.08814/) (2023)
- [SPVSoAP3D: A Second-order Average Pooling Approach to enhance 3D Place Recognition in Horticultural Environments](https://mlsanity.com/paper/2410.17017/) (2024)
- [From General Vision to Reliable Traversability Estimation: Adapting Vision Foundation Models for Unstructured Outdoor Environments](https://mlsanity.com/paper/2605.29565/) (2026)
- [Uncertainty-aware Perception Models for Off-road Autonomous Unmanned Ground Vehicles](https://mlsanity.com/paper/2209.11115/) (2022)
- [Analysis of LiDAR Configurations on Off-road Semantic Segmentation Performance](https://mlsanity.com/paper/2306.16551/) (2023)
- [TE-NeXt: A LiDAR-Based 3D Sparse Convolutional Network for Traversability Estimation](https://mlsanity.com/paper/2406.01395/) (2024)
- [Task-Oriented Pre-Training for Drivable Area Detection](https://mlsanity.com/paper/2409.20166/) (2024)
- [Panoptic-CUDAL: Rural Australia Point Cloud Dataset in Rainy Conditions](https://mlsanity.com/paper/2503.16378/) (2025)
- [TOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions](https://mlsanity.com/paper/2506.21630/) (2025)
- [Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey](https://mlsanity.com/paper/2601.22198/) (2026)
- [LiteViLNet: Lightweight Vision-LiDAR Fusion Network for Efficient Road Segmentation](https://mlsanity.com/paper/2605.21007/) (2026)
- [Pseudo-Trilateral Adversarial Training for Domain Adaptive Traversability Prediction](https://mlsanity.com/paper/2306.14370/) (2023)
- [3DTTNet: Multimodal Fusion-Based 3D Traversable Terrain Modeling for Off-Road Environments](https://mlsanity.com/paper/2412.08195/) (2024)
- [Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing](https://mlsanity.com/paper/2502.06219/) (2025)
- [OT-Drive: Out-of-Distribution Off-Road Traversable Area Segmentation via Optimal Transport](https://mlsanity.com/paper/2601.09952/) (2026)

## Abstract (arXiv)

Freespace detection is an essential component of autonomous driving technology and plays an important role in trajectory planning. In the last decade, deep learning-based free space detection methods have been proved feasible. However, these efforts were focused on urban road environments and few deep learning-based methods were specifically designed for off-road free space detection due to the lack of off-road benchmarks. In this paper, we present the ORFD dataset, which, to our knowledge, is the first off-road free space detection dataset. The dataset was collected in different scenes (woodland, farmland, grassland, and countryside), different weather conditions (sunny, rainy, foggy, and snowy), and different light conditions (bright light, daylight, twilight, darkness), which totally contains 12,198 LiDAR point cloud and RGB image pairs with the traversable area, non-traversable area and unreachable area annotated in detail. We propose a novel network named OFF-Net, which unifies Transformer architecture to aggregate local and global information, to meet the requirement of large receptive fields for free space detection tasks. We also propose the cross-attention to dynamically fuse LiDAR and RGB image information for accurate off-road free space detection. Dataset and code are publicly available athttps://github.com/chaytonmin/OFF-Net.