通过颜色校正与噪声抑制，提升户外场景语义分割鲁棒性
Technical Report for ICRA 2025 GOOSE 2D Semantic Segmentation Challenge: Leveraging Color Shift Correction, RoPE-Swin Backbone, and Quantile-based Label Denoising Strategy for Robust Outdoor Scene Understanding
- 采用带旋转位置编码的Swin Transformer增强空间泛化能力
- 在GOOSE测试集上实现84.8%的mIoU，优于基准模型
- 适合关注真实环境分割与模型稳定性的研究者
本报告介绍团队ACVLAB为ICRA 2025 GOOSE 2D语义分割挑战赛开发的分割框架，旨在真实环境下将户外场景解析为九类语义。方法融合了引入旋转位置编码（RoPE）的Swin Transformer主干网络，以提升空间泛化能力，并设计了颜色偏移估计与校正模块，用于补偿自然环境中光照不一致问题。为进一步提升训练稳定性，采用基于分位数的去噪策略，对误差最高的前2.5%像素进行降权处理，将其视为噪声并抑制其优化影响。在官方GOOSE测试集上，该方法达到0.848的平均交并比（mIoU），验证了颜色校正、位置编码与误差感知去噪相结合在鲁棒语义分割中的有效性。
原文摘要 · Abstract (English)
This report presents our semantic segmentation framework developed by team ACVLAB for the ICRA 2025 GOOSE 2D Semantic Segmentation Challenge, which focuses on parsing outdoor scenes into nine semantic categories under real-world conditions. Our method integrates a Swin Transformer backbone enhanced with Rotary Position Embedding (RoPE) for improved spatial generalization, alongside a Color Shift Estimation-and-Correction module designed to compensate for illumination inconsistencies in natural environments. To further improve training stability, we adopt a quantile-based denoising strategy that downweights the top 2.5\% of highest-error pixels, treating them as noise and suppressing their influence during optimization. Evaluated on the official GOOSE test set, our approach achieved a mean Intersection over Union (mIoU) of 0.848, demonstrating the effectiveness of combining color correction, positional encoding, and error-aware denoising in robust semantic segmentation.
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