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arXiv · 2506.15560

RaCalNet: Radar Calibration Network for Sparse-Supervised Metric Depth Estimation

Xingrui Qin·Wentao Zhao·Chuan Cao·Yihe Niu·Tianchen Deng·Houcheng Jiang·Rui Guo·Jingchuan Wang
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Abstract

Dense depth estimation using millimeter-wave radar typically requires dense LiDAR supervision, generated via multi-frame projection and interpolation, for guiding the learning of accurate depth from sparse radar measurements and RGB images. However, this paradigm is both costly and data-intensive. To address this, we propose RaCalNet, a novel framework that eliminates the need for dense supervision by using sparse LiDAR to supervise the learning of refined radar measurements, resulting in a supervision density of merely around 1\% compared to dense-supervised methods. RaCalNet is composed of two key modules. The Radar Recalibration module performs radar point screening and pixel-wise displacement refinement, producing accurate and reliable depth priors from sparse radar inputs. These priors are then used by the Metric Depth Optimization module, which learns to infer scene-level scale priors and fuses them with monocular depth predictions to achieve metrically accurate outputs. This modular design enhances structural consistency and preserves fine-grained geometric details. Despite relying solely on sparse supervision, RaCalNet produces depth maps with clear object contours and fine-grained textures, demonstrating superior visual quality compared to state-of-the-art dense-supervised methods. Quantitatively, it achieves performance comparable to existing methods on the ZJU-4DRadarCam dataset and yields a 34.89\% RMSE reduction in real-world deployment scenarios. We plan to gradually release the code and models in the future at https://github.com/818slam/RaCalNet.git.

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Xingrui Qin, Wentao Zhao, Chuan Cao, Yihe Niu, Tianchen Deng, Houcheng Jiang, Rui Guo, Jingchuan Wang. 2025-07-05. RaCalNet: Radar Calibration Network for Sparse-Supervised Metric Depth Estimation. https://arxiv.org/abs/2506.15560

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