JustDepth: Real-Time Radar-Camera Depth Estimation with Single-Scan LiDAR Supervision

Abstract: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.

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2607.22172 [cs.CV]

(or arXiv:2607.22172v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.22172 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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