{"ID":2824654,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.22972","arxiv_id":"2512.22972","title":"Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection","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.","short_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 pr...","url_abs":"https://arxiv.org/abs/2512.22972","url_pdf":"https://arxiv.org/pdf/2512.22972v2","authors":"[\"Runwei Guan\",\"Jianan Liu\",\"Shaofeng Liang\",\"Fangqiang Ding\",\"Shanliang Yao\",\"Xiaokai Bai\",\"Daizong Liu\",\"Tao Huang\",\"Guoqiang Mao\",\"Hui Xiong\"]","published":"2025-12-28T15:32:17Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"eess.SP\"]","methods":"[]","has_code":false}