仅用单次激光雷达监督，实现雷达相机实时高精度深度估计
JustDepth: Real-Time Radar-Camera Depth Estimation with Single-Scan LiDAR Supervision
- 单阶段设计，融合雷达1D特征与图像，无需多步处理
- 推理速度提升39.7倍，垂直水平梯度比降低66%（VHGR）
- 适合自动驾驶系统，兼顾精度与低延迟需求
在自动驾驶感知中，精确且低延迟的深度信息至关重要。相机提供丰富外观但缺乏度量尺度，而车载雷达虽具度量距离却稀疏且噪声大。现有方法多为多阶段或依赖额外标注，增加延迟并影响可移植性。本文提出JustDepth，一种仅使用雷达、相机和单次扫描激光雷达训练的单阶段深度估计算法。所有雷达回波被聚合为固定宽度的一维表示，解耦运行时与点云数量。高度融合模块融合多模态信息，轻量级图神经网络全局传播深度，训练专用置信度解码器稳定学习且无测试开销。通过简单增强缓解条纹伪影，并用垂直-水平梯度比（VHGR）量化。在nuScenes数据集上，相比最新方法，JustDepth保持精度的同时，推理时间减少39.7倍，条纹伪影降低66%（以VHGR衡量）。
原文摘要 · Abstract (English)
Accurate yet low-latency depth is essential for radar-camera perception in autonomous systems. Cameras provide rich appearance but lack metric scale, whereas automotive radar offers metric range but is sparse and noisy. Many pipelines are multi-stage or depend on auxiliary annotations, increasing latency and limiting portability. We introduce JustDepth, a single-stage radar-camera depth estimator trained only with radar, camera, and single-scan LiDAR. All radar returns are aggregated into a fixed-width 1D representation, decoupling runtime from point count. A Height Fusion Block fuses modalities, a lightweight GNN propagates depth globally, and a training-only confidence decoder stabilizes learning with zero test-time cost. We mitigate stripe artifacts via simple augmentations and quantify them using the Vertical-Horizontal Gradient Ratio (VHGR). On nuScenes, compared to recent state-of-the-art methods, JustDepth maintains accuracy while reducing inference time by 39.7x and stripe artifacts by 66% as measured by VHGR.
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