ReManNet：面向单目3D车道检测的黎曼流形网络
ReManNet: A Riemannian Manifold Network for Monocular 3D Lane Detection
把车道几何编码成 SPD 流形上的黎曼高斯描述子，再用轻量门控与视觉特征融合，为单目 2D-to-3D 提升提供不变几何-拓扑约束；3D-TLIoU 这种基于管状邻域切片重叠的曲线级损失可直接迁移到其他曲线/线状结构（车道、血管、道路边界）的检测与对齐任务。
原文依据 encodes geometry as Riemannian Gaussian descriptors on the symmetric positive-definite (SPD) manifold
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论单目3D车道检测由于深度歧义和几何约束薄弱而仍然具有挑战性。主流方法依赖深度引导、鸟瞰图投影以及基于锚点或曲线的检测头，并采用简化的物理假设，在仅对道路几何进行弱编码的同时对高维图像特征进行重映射。由于缺乏车道与底层路面之间不变的几何-拓扑耦合，2D到3D的提升是病态且脆弱的，常常退化为凹陷、凸起和扭曲。为解决这一问题，我们提出道路流形假设：道路是R3中的一个光滑二维流形，车道是嵌入其中的一维子流形，采样得到的车道点是稠密观测，从而在曲面、曲线和点集之间耦合度量与拓扑。在此基础上，我们提出ReManNet，它首先利用图像骨干网络和检测头产生初始车道预测，然后将几何编码为对称正定（SPD）流形上的黎曼高斯描述子，并通过轻量门控将这些描述子与视觉特征融合，以保持连贯的3D推理。我们还提出3D隧道车道IoU（3D-TLIoU）损失，这是一种联合点-曲线的目标函数，通过计算沿每条车道的管状邻域的逐切片重叠来改善形状级对齐。在标准基准上的大量实验表明，ReManNet取得了最优或具有竞争力的结果。在OpenLane上，其F1较基线提升+8.2%，较此前最优提升+1.8%，场景级增益最高达+6.6%。代码将公开于https://github.com/changehome717/ReManNet。
英文原文摘要 1661 字符
Monocular 3D lane detection remains challenging due to depth ambiguity and weak geometric constraints. Mainstream methods rely on depth guidance, BEV projection, and anchor- or curve-based heads with simplified physical assumptions, remapping high-dimensional image features while only weakly encoding road geometry. Lacking an invariant geometric-topological coupling between lanes and the underlying road surface, 2D-to-3D lifting is ill-posed and brittle, often degenerating into concavities, bulges, and twists. To address this, we propose the Road-Manifold Assumption: the road is a smooth 2D manifold in R3, lanes are embedded 1D submanifolds, and sampled lane points are dense observations, thereby coupling metric and topology across surfaces, curves, and point sets. Building on this, we propose ReManNet, which first produces initial lane predictions with an image backbone and detection heads, then encodes geometry as Riemannian Gaussian descriptors on the symmetric positive-definite (SPD) manifold, and fuses these descriptors with visual features through a lightweight gate to maintain coherent 3D reasoning. We also propose the 3D Tunnel Lane IoU (3D-TLIoU) loss, a joint point-curve objective that computes slice-wise overlap of tubular neighborhoods along each lane to improve shape-level alignment. Extensive experiments on standard benchmarks demonstrate that ReManNet achieves state-of-the-art (SOTA) or competitive results. On OpenLane, it improves F1 by +8.2% over the baseline and by +1.8% over the previous best, with scenario-level gains of up to +6.6%. The code will be publicly available at https://github.com/changehome717/ReManNet.