Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection
This work addresses robust 3D object detection for autonomous driving and robot perception, particularly in adverse weather, but it is incremental as it builds on existing fusion methods with specific enhancements.
The paper tackles the problem of information loss and high computational cost in 4D radar-based 3D object detection by proposing WRCFormer, a framework that fuses raw 4D radar tensors with camera images, achieving state-of-the-art performance with improvements of 2.4% in all scenarios and 1.6% in sleet conditions on the K-Radar benchmark.
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.