# 用于语义分割的RGB与互补模态多模态融合

> 原文链接：https://robotics.hellobike.com/papers/2026-10-13-2610-17800
> 频道：papers | 更新日期：2026-10-13 | 标签：arxiv, cs.RO
> 核心摘要：针对RGB-X语义分割中的跨传感器特征失配与融合不稳，提出BPCF模块，并在六类模态配对和八个公开数据集上验证。

# 用于语义分割的RGB与互补模态多模态融合

::: v-pre
> 原文链接：[Multimodal Fusion of RGB and Complementary Modalities for Semantic Segmentation](https://arxiv.org/abs/2610.17800)（arXiv 2610.17800 · 发布日期：2026-10-13）

**Multimodal Fusion of RGB and Complementary Modalities for Semantic Segmentation**

针对RGB-X语义分割中的跨传感器特征失配与融合不稳，提出BPCF模块，并在六类模态配对和八个公开数据集上验证。

## 核心要点

- RGB-X加入热成像、LiDAR等辅助流，可提升弱光和运动模糊下鲁棒性。
- 不同传感器特征常不匹配，导致融合过程不稳定，是主要动机。
- BPCF以双向、极性感知的跨模态融合捕获互补线索并增强特征对齐。
- 模型在八个公开数据集上达到SOTA，代码和训练模型接收后发布。

**验证范围与局限**：验证范围为六类RGB-X模态配对及八个公开数据集。

## 英文摘要 (Abstract)

Multi-modal semantic segmentation adds an auxiliary sensing stream to RGB images (RGB-X), such as thermal, LiDAR, depth, event, polarization, or light field. This can improve robustness in poor lighting and under motion blur. But different sensors often produce features that do not match well, and fusion can become unstable. To alleviate these issues, we propose a bidirectional polarity-aware cross-modality fusion (BPCF) module that effectively captures complementary cues while enhancing feature alignment. We evaluate the framework on six modality pairings: RGB-Thermal, RGB-LiDAR, RGB-Depth, RGB-Event, RGB-Polarization, and RGB-Light Field. Our model achieve state-of-the-art (SOTA) results on eight public datasets, including MFNet, KITTI-360, DELIVER, DDD17, DSEC, MCubeS, ZJU, and UrbanLF. The codes and trained models will be released upon acceptance.

作者：Hu Cao, Yifeng Cheng, Mu He, Mengyu Li, Yinlong Liu, Alois Knoll
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## 原始参考来源 (Sources)

- [arXiv 2610.17800：Multimodal Fusion of RGB and Complementary Modalities for Semantic Segmentation](https://arxiv.org/abs/2610.17800)