The recent advancements in deep convolutional neural networks have shown significant promise in the domain of road scene parsing. Nevertheless, the existing works focus primarily on freespace detection, with little attention given to hazardous road defects that could compromise both driving safety and comfort. In this paper, we introduce RoadFormer, a novel Transformer-based data-fusion network developed for road scene parsing. RoadFormer utilizes a duplex encoder architecture to extract heterogeneous features from both RGB images and surface normal information. The encoded features are subsequently fed into a novel heterogeneous feature synergy block for effective feature fusion and recalibration. The pixel decoder then learns multi-scale long-range dependencies from the fused and recalibrated heterogeneous features, which are subsequently processed by a Transformer decoder to produce the final semantic prediction. Additionally, we release SYN-UDTIRI, the first large-scale road scene parsing dataset that contains over 10,407 RGB images, dense depth images, and the corresponding pixel-level annotations for both freespace and road defects of different shapes and sizes. Extensive experimental evaluations conducted on our SYN-UDTIRI dataset, as well as on three public datasets, including KITTI road, CityScapes, and ORFD, demonstrate that RoadFormer outperforms all other state-of-the-art networks for road scene parsing. Specifically, RoadFormer ranks first on the KITTI road benchmark. Our source code, created dataset, and demo video are publicly available at mias.group/RoadFormer.

翻译：深度卷积神经网络的最新进展已在道路场景解析领域展现出显著潜力。然而，现有研究主要聚焦于自由空间检测，鲜有关注可能危及行车安全与舒适性的危险道路缺陷。本文提出RoadFormer——一种新型基于Transformer的数据融合网络，专用于道路场景解析。该网络采用双编码器架构，分别从RGB图像与表面法线信息中提取异构特征；随后将编码后的特征输入新型异构特征协同模块，实现高效特征融合与重校准。像素解码器从融合并重校准后的异构特征中学习多尺度长程依赖关系，再经Transformer解码器处理以生成最终语义预测。此外，我们发布了SYN-UDTIRI——首个包含10,407张RGB图像、密集深度图像及对应像素级标注的大规模道路场景解析数据集，其标注覆盖不同形状与尺寸的自由空间及道路缺陷。基于自有SYN-UDTIRI数据集及KITTI道路、CityScapes、ORFD三个公开数据集的广泛实验评估表明，RoadFormer在所有现有最优道路场景解析网络中表现最优，并在KITTI道路基准上排名第一。我们的源代码、创建的数据集及演示视频已公开于mias.group/RoadFormer。