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arXiv · 2512.22972

Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection

Runwei Guan·Jianan Liu·Shaofeng Liang·Fangqiang Ding·Shanliang Yao·Xiaokai Bai·Daizong Liu·Tao Huang·Guoqiang Mao·Hui Xiong
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Abstract

4D millimeter-wave (mmWave) radar has been widely adopted in autonomous driving and robot perception due to its low cost and all-weather robustness. However, point-cloud-based radar representations suffer from information loss due to multi-stage signal processing, while directly utilizing raw 4D radar tensors incurs prohibitive computational costs. To address these challenges, we propose WRCFormer, a novel 3D object detection framework that efficiently fuses raw 4D radar cubes with camera images via decoupled multi-view radar representations. Our approach introduces two key components: (1) A Wavelet Attention Module embedded in a wavelet-based Feature Pyramid Network (FPN), which enhances the representation of sparse radar signals and image data by capturing joint spatial-frequency features, thereby mitigating information loss while maintaining computational efficiency. (2) A Geometry-guided Progressive Fusion mechanism, a two-stage query-based fusion strategy that progressively aligns multi-view radar and visual features through geometric priors, enabling modality-agnostic and efficient integration without overwhelming computational overhead. Extensive experiments on the K-Radar benchmark show that WRCFormer achieves state-of-the-art performance, surpassing the best existing model by approximately 2.4% in all scenarios and 1.6% in sleet conditions, demonstrating strong robustness in adverse weather.

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Runwei Guan, Jianan Liu, Shaofeng Liang, Fangqiang Ding, Shanliang Yao, Xiaokai Bai, Daizong Liu, Tao Huang, Guoqiang Mao, Hui Xiong. 2025-12-28. Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection. https://arxiv.org/abs/2512.22972

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