通过雷达与激光雷达相互增强，提升复杂环境下3D目标检测精度。
MutualForce: Mutual-Aware Enhancement for 4D Radar-LiDAR 3D Object Detection
- 雷达特征引导双方几何学习，激光雷达形状信息补足雷达稀疏缺陷。
- 在VoD数据集上达到71.76% mAP，驾驶走廊内达86.36%。
- 特别提升车辆检测性能，准确率分别提高4.17%和4.20%。
雷达与激光雷达在自动驾驶中广泛应用：激光雷达提供丰富结构信息，雷达则在恶劣天气下表现更稳健。近期研究证明融合两者点云的有效性，但模态错位与特征提取过程中的信息丢失仍是挑战。为此，本文提出一种4D雷达-激光雷达框架，实现双向特征增强。首先，利用雷达的指示性特征引导雷达与激光雷达的几何特征学习；随后，为缓解二者稀疏性差异，采用激光雷达的形状信息增强雷达的鸟瞰图（BEV）特征。在View-of-Delft（VoD）数据集上的大量实验表明，本方法优于现有方法，在全区域实现71.76%的mAP，驾驶走廊内达到86.36%。尤其对车辆检测，得益于强指示性特征与对称形状，平均精度（AP）分别提升4.17%和4.20%。
原文摘要 · Abstract (English)
Radar and LiDAR have been widely used in autonomous driving as LiDAR provides rich structure information, and radar demonstrates high robustness under adverse weather. Recent studies highlight the effectiveness of fusing radar and LiDAR point clouds. However, challenges remain due to the modality misalignment and information loss during feature extractions. To address these issues, we propose a 4D radar-LiDAR framework to mutually enhance their representations. Initially, the indicative features from radar are utilized to guide both radar and LiDAR geometric feature learning. Subsequently, to mitigate their sparsity gap, the shape information from LiDAR is used to enrich radar BEV features. Extensive experiments on the View-of-Delft (VoD) dataset demonstrate our approach's superiority over existing methods, achieving the highest mAP of 71.76% across the entire area and 86.36\% within the driving corridor. Especially for cars, we improve the AP by 4.17% and 4.20% due to the strong indicative features and symmetric shapes.
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