A Cross-Scale Decoder with Token Refinement for Off-Road Semantic Segmentation
Off-road semantic segmentation is fundamentally challenged by irregular terrain, vegetation clutter, and inherent annotation ambiguity. Unlike urban scenes with crisp object boundaries, off-road environments exhibit strong class-level similarity among terrain categories, resulting in thick and uncertain transition regions that degrade boundary coherence and destabilize training. Rare or thin structures, such as narrow traversable gaps or isolated obstacles, further receive sparse and unreliable supervision and are easily overwhelmed by dominant background textures. Existing decoder designs either rely on low-scale bottlenecks that oversmooth fine structural details, or repeatedly fuse hig...
| Authors | Seongkyu Choi Jhonghyun An |
|---|---|
| Primary category | cs.CV |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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