Reliable segmentation of road lines and markings is critical to autonomous driving. Our work is motivated by the observations that road lines and markings are (1) frequently occluded in the presence of moving vehicles, shadow, and glare and (2) highly structured with low intra-class shape variance and overall high appearance consistency. To solve these issues, we propose a Homography Guided Fusion (HomoFusion) module to exploit temporally-adjacent video frames for complementary cues facilitating the correct classification of the partially occluded road lines or markings. To reduce computational complexity, a novel surface normal estimator is proposed to establish spatial correspondences between the sampled frames, allowing the HomoFusion module to perform a pixel-to-pixel attention mechanism in updating the representation of the occluded road lines or markings. Experiments on ApolloScape, a large-scale lane mark segmentation dataset, and ApolloScape Night with artificial simulated night-time road conditions, demonstrate that our method outperforms other existing SOTA lane mark segmentation models with less than 9\% of their parameters and computational complexity. We show that exploiting available camera intrinsic data and ground plane assumption for cross-frame correspondence can lead to a light-weight network with significantly improved performances in speed and accuracy. We also prove the versatility of our HomoFusion approach by applying it to the problem of water puddle segmentation and achieving SOTA performance.

翻译：道路标线的可靠分割对自动驾驶至关重要。我们的工作基于以下观察：（1）道路标线常被移动车辆、阴影和眩光遮挡；（2）道路标线具有高度结构化特征，类内形状方差低且整体表观一致性高。为解决这些问题，我们提出单应性引导融合（HomoFusion）模块，利用时序相邻视频帧的互补线索促进部分遮挡道路标线的正确分类。为降低计算复杂度，提出新颖的表面法线估计器建立采样帧间的空间对应关系，使HomoFusion模块能通过像素级注意力机制更新被遮挡道路标线的表征。在大型车道线分割数据集ApolloScape及人工模拟夜间道路条件的ApolloScape Night上的实验表明，本方法在参数数量和计算复杂度不足现有最先进车道线分割模型9%的情况下仍能超越其性能。我们证明利用相机内参数据和地平面假设进行跨帧对应可构建轻量级网络，显著提升推理速度与精度。通过将本方法应用于水坑分割任务并取得最优性能，验证了HomoFusion方法的通用性。