Abstract
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 high-detail features, which tends to amplify annotation noise and incur substantial computational cost. We present a cross-scale decoder that explicitly addresses these challenges through three complementary mechanisms. First, a global--local token refinement module consolidates semantic context on a compact bottleneck lattice, guided by boundary-aware regularization to remain robust under ambiguous supervision. Second, a gated detail bridge selectively injects fine-scale structural cues only once through cross-scale attention, preserving boundary and texture information while avoiding noise accumulation. Third, an uncertainty-guided class-aware point refinement selectively updates the least reliable pixels, improving rare and ambiguous structures with minimal computational overhead. The resulting framework achieves noise-robust and boundary-preserving segmentation tailored to off-road environments, recovering fine structural details while maintaining deployment-friendly efficiency. Experimental results on standard off-road benchmarks demonstrate consistent improvements over prior approaches without resorting to heavy dense feature fusion.
Abstract (translated)
越野场景语义分割面临不规则地形、植被杂乱及固有标注模糊等根本性挑战。与具有清晰物体边界的城市场景不同，越野环境中地形类别间存在强烈的类间相似性，导致模糊且不确定的过渡区域，这削弱了边界连贯性并 destabilizes 训练过程。稀有或细长结构（如狭窄可通行间隙或孤立障碍物）进一步受到稀疏且不可靠的监督，并容易被主导性背景纹理淹没。现有解码器设计要么依赖会过度平滑精细结构细节的低尺度瓶颈，要么反复融合高细节特征，这往往会放大标注噪声并产生巨大的计算成本。我们提出了一种跨尺度解码器，通过三种互补机制明确应对这些挑战。首先，全局-局部标记细化模块在紧凑的瓶颈栅格上整合语义上下文，由边界感知正则化引导，以在模糊监督下保持鲁棒性。其次，门控细节桥接通过跨尺度注意力仅注入一次细尺度结构线索，在保留边界和纹理信息的同时避免噪声累积。第三，不确定性引导的类感知点细化选择性地更新最不可靠的像素，以最小的计算开销改善稀有和模糊结构。所得框架实现了针对越野环境的噪声鲁棒且边界保持的分割，在保持部署友好效率的同时恢复了精细结构细节。在标准越野基准上的实验结果表明，无需依赖沉重的密集特征融合，该方法即持续优于先前方法。
URL
https://arxiv.org/abs/2603.27931
