ROD: RGB-Only Fast and Efficient Off-road Freespace Detection
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911102388928512 |
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| author | Sun, Tong Ye, Hongliang Mei, Jilin Chen, Liang Zhao, Fangzhou Zong, Leiqiang Hu, Yu |
| author_facet | Sun, Tong Ye, Hongliang Mei, Jilin Chen, Liang Zhao, Fangzhou Zong, Leiqiang Hu, Yu |
| contents | Off-road freespace detection is more challenging than on-road scenarios because of the blurred boundaries of traversable areas. Previous state-of-the-art (SOTA) methods employ multi-modal fusion of RGB images and LiDAR data. However, due to the significant increase in inference time when calculating surface normal maps from LiDAR data, multi-modal methods are not suitable for real-time applications, particularly in real-world scenarios where higher FPS is required compared to slow navigation. This paper presents a novel RGB-only approach for off-road freespace detection, named ROD, eliminating the reliance on LiDAR data and its computational demands. Specifically, we utilize a pre-trained Vision Transformer (ViT) to extract rich features from RGB images. Additionally, we design a lightweight yet efficient decoder, which together improve both precision and inference speed. ROD establishes a new SOTA on ORFD and RELLIS-3D datasets, as well as an inference speed of 50 FPS, significantly outperforming prior models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08697 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ROD: RGB-Only Fast and Efficient Off-road Freespace Detection Sun, Tong Ye, Hongliang Mei, Jilin Chen, Liang Zhao, Fangzhou Zong, Leiqiang Hu, Yu Computer Vision and Pattern Recognition Off-road freespace detection is more challenging than on-road scenarios because of the blurred boundaries of traversable areas. Previous state-of-the-art (SOTA) methods employ multi-modal fusion of RGB images and LiDAR data. However, due to the significant increase in inference time when calculating surface normal maps from LiDAR data, multi-modal methods are not suitable for real-time applications, particularly in real-world scenarios where higher FPS is required compared to slow navigation. This paper presents a novel RGB-only approach for off-road freespace detection, named ROD, eliminating the reliance on LiDAR data and its computational demands. Specifically, we utilize a pre-trained Vision Transformer (ViT) to extract rich features from RGB images. Additionally, we design a lightweight yet efficient decoder, which together improve both precision and inference speed. ROD establishes a new SOTA on ORFD and RELLIS-3D datasets, as well as an inference speed of 50 FPS, significantly outperforming prior models. |
| title | ROD: RGB-Only Fast and Efficient Off-road Freespace Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.08697 |