# Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection Source: [Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection]() Collection index: [llms.txt]() Data updated: 2026-01-17T05:00:05.253Z Authors: Runwei Guan, Jianan Liu, Shaofeng Liang, Fangqiang Ding, Shanliang Yao, Xiaokai Bai, Daizong Liu, Tao Huang, Guoqiang Mao, Hui Xiong Published: 2025-12-28T15:32:17.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Object Detection on K-Radar (Weather Conditions)](): 3D Object Detection; K-Radar; AP3D Total: 58.7. - [3D Object Detection on K-Radar v2.0 (test)](): 3D Object Detection; K-Radar v2.0 (test); AP (Sedan): 56.4. - [3D Object Detection on K-Radar Sedan v1.1 (test)](): 3D Object Detection; K-Radar Sedan v1.1 (test); Total AP: 58.7. ## Original sources and implementation [Original paper]() [PDF]()