摘要： 道路凹坑的实时高精度感知与三维重建是保障自动驾驶行车安全与主动悬架控制的核心技术。现有的道路凹坑感知方法主要依赖单一视觉模态或静态跨模态融合策略。单一视觉方法易受剧烈光照变化、弱纹理路面及阴影遮挡的干扰，难以稳定获取鲁棒的深度信息；而基于激光雷达点云的方法受限于传感器线束分布，在远距离或高速行驶场景下点云极度稀疏，导致凹坑边缘的微观几何特征表达不充分。现有的跨模态融合方案多采用全局静态拼接，未充分考虑异构数据间的空间非线性配准误差，在传感器时空错位时易导致特征污染或几何有效信息的淹没，使得凹坑边界的三维重建精度受限。针对上述问题，本文提出一种基于激光雷达与深度相机跨模态融合的质心引导道路凹坑感知与三维重建方法（DeepRoad）。 本文方法构建了一个端到端的深度神经网络，主要包含三个核心技术阶段。第一阶段，多源异构特征粗对齐与多尺度提取。系统并行输入深度相机采集的二维图像与激光雷达生成的空间点云，利用最近邻自适应粗匹配模块，在时空尺度上建立像素坐标系与点云投影矩阵的初步关联，并通过多尺度特征提取网络捕获两者的底层表征。第二阶段，质心引导的非线性特征精细融合。针对空间配准误差导致的特征漂移，本文创新性地引入了质心引导机制。通过轻量化预测网络定位候选凹坑区域的几何质心，并以该质心作为空间锚点构建三维非对称注意力网络，动态调整跨模态特征的交互权重，实现图像高频纹理与稀疏点云微观几何信息在局部高价值区域的自适应聚集与深度融合。第三阶段，鸟瞰图（BEV）空间映射与边缘语义一致性约束。为了解决下采样操作带来的凹坑边界模糊与拓扑失真问题，DeepRoad将融合后的跨模态高维特征统一投影至鸟瞰图空间以保持全局拓扑结构，并设计了一种新颖的边缘语义一致性正则化损失函数。该损失函数通过显示强化凹坑物理边界与其周围正常路面之间的空间连续性与语义对立性，有效抑制了重建伪影，大幅提升了凹坑边缘的拟合精度。 为了验证DeepRoad方法的有效性与鲁棒性，本文在合成数据集SYN-UDTIRI与真实道路数据集RSRD上进行了充分的对比实验与消融实验。在SYN-UDTIRI数据集上的道路凹坑分割对比实验中，DeepRoad在关键指标上均取得了最优性能，其最大F1分数（MaxF）达到97.58%，平均精度（AP）达到94.04%，准确率（Pre）为97.53%，召回率（Rec）达到97.86%，全面超越了RoadFormer、SNE-RoadSeg+等主流跨模态融合方法，尤其在边界和细节的刻画上展现出显著的召回率优势。结构模块消融实验表明，在基础跨模态融合网络（Baseline）中逐步引入质心引导等模块，验证了各模块的有效性与互补性。此外，在真实道路数据集RSRD的泛化与鲁棒性测试中，DeepRoad在传感器噪声与点云稀疏干扰下依旧保持稳定的几何估计能力，实现了0.479 cm的绝对误差和0.568 cm的均方根误差（RMSE），大误差（>0.5 cm）比例仅为35.1%，相较于先进的RoadBEV 模型各项指标均表现更优，极端偏差情况显著减少，未出现误差放大现象。 综上所述，本文所提出的DeepRoad方法通过质心引导机制，成功攻克了复杂场景下道路凹坑三维感知边界模糊、异构特征难对齐的技术瓶颈，形成了一套系统化的端到端感知与重建解决方案，为自动驾驶智能避障规划提供了高精度的数据支撑，具有重要的理论创新与工程应用价值。未来的研究工作将聚焦于网络模型的轻量化剪枝与边缘端实时部署，并探索将DeepRoad与多模态视觉-语言大模型（VLM）深度融合，以期推动无人驾驶技术在全天候、全场景下的智能决策与可靠进化。

Abstract: Real-time, high-precision perception and 3D reconstruction of road potholes are core technologies for ensuring driving safety and active suspension control in autonomous driving. Existing road pothole perception methods primarily rely on a single visual modality or static cross-modal fusion strategies. Single visual methods are susceptible to drastic illumination changes, texture-less road surfaces, and shadow occlusions, making it difficult to stably acquire robust depth information. Meanwhile, 3D point cloud-based methods are limited by the beam distribution of LiDAR sensors, resulting in extremely sparse point clouds in long-distance or high-speed driving scenarios, which leads to insufficient expression of the microscopic geometric features at pothole boundaries. Existing cross-modal fusion schemes mostly adopt global static concatenation, which fails to fully consider the non-linear spatial registration errors between heterogeneous data. This easily leads to feature contamination or the drowning of valid geometric information when sensors suffer from spatio-temporal misalignment, thereby limiting the 3D reconstruction accuracy of pothole boundaries. To address these problems, this paper proposes a centroid-guided road pothole perception and 3D reconstruction method based on the cross-modal fusion of LiDAR and depth camera, termed DeepRoad. The proposed method constructs an end-to-end deep neural network, which mainly comprises three core technical stages. In the first stage, multi-source heterogeneous feature rough alignment and multi-scale extraction are performed. The system concurrently inputs 2D images captured by the depth camera and spatial point clouds generated by the LiDAR. A nearest-neighbor adaptive rough matching module is utilized to establish a preliminary correlation between the pixel coordinate system and the point cloud projection matrix across spatio-temporal scales, and the underlying representations of both modalities are captured via a multi-scale feature extraction network. In the second stage, centroid-guided non-linear feature fine fusion is executed. To handle feature drift caused by spatial registration errors, a centroid guidance mechanism is innovatively introduced. A lightweight prediction network is designed to localize the geometric centroid of candidate pothole regions. Taking this centroid as a spatial anchor, a 3D asymmetric attention network is constructed to dynamically adjust the interaction weights of cross-modal features, achieving adaptive aggregation and deep fusion of high-frequency image textures and sparse point cloud microscopic geometry in high-value local regions. In the third stage, Bird's-Eye-View (BEV) space mapping and edge semantic consistency constraints are applied. To resolve the boundary blurring and topological distortion issues caused by downsampling operations, DeepRoad projects the fused cross-modal high-dimensional features uniformly into the BEV space to preserve the global topological structure, and designs a novel edge semantic consistency regularization loss function. By explicitly enforcing spatial continuity and semantic opposition between the physical boundaries of potholes and the surrounding normal road surface, this loss function effectively suppresses reconstruction artifacts and substantially improves the fitting precision of pothole edges. To verify the effectiveness and robustness of the DeepRoad method, extensive comparative and ablation experiments were conducted on the synthetic dataset SYN-UDTIRI and the real-world road dataset RSRD. In the road pothole segmentation comparative experiments on the SYN-UDTIRI dataset, DeepRoad achieves the optimal performance across all key metrics: its maximum F1-score (MaxF) reaches 97.58%, Average Precision (AP) reaches 94.04%, Precision (Pre) is 97.53%, and Recall (Rec) reaches 97.86%, comprehensively outperforming mainstream cross-modal fusion methods such as RoadFormer and SNE-RoadSeg+, demonstrating a significant recall advantage particularly in depicting boundaries and fine details. Structural module ablation experiments indicate that progressively introducing modules such as centroid guidance into the baseline cross-modal fusion network validates the effectiveness and complementarity of each module. In the candidate region construction strategy ablation, the model reflecting excellent structural independence and stability. Furthermore, in the generalization and robustness tests on the real road dataset RSRD, DeepRoad maintains stable geometric estimation capabilities under sensor noise and sparse point cloud interference, achieving an absolute error of 0.479 cm and a root mean square error (RMSE) of 0.568 cm, with the proportion of large errors (>0.5 cm) restricted to only 35.1%. Compared with the state-of-the-art RoadBEV model, all metrics of the proposed method are superior, extreme deviations are significantly reduced, and no error amplification occurs. In conclusion, the DeepRoad method proposed in this paper successfully overcomes the technical bottlenecks of blurred 3D perception boundaries and difficult alignment of heterogeneous features for road potholes in complex scenarios through the centroid-guidance mechanism. It forms a systematic end-to-end perception and reconstruction solution, providing high-precision data support for intelligent obstacle avoidance planning in autonomous driving, and possessing important theoretical innovation and engineering application value. Future research will focus on network model lightweight pruning and edge-side real-time deployment, as well as exploring the deep integration of DeepRoad with multi-modal Vision-Language Models (VLMs) to drive the intelligent decision-making and reliable evolution of autonomous driving technology under all-weather and all-scene conditions.