通过统一3D表征实现细粒度融合，提升多模态3D目标检测精度
FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection
- 用统一3D表征融合点云与图像特征，解决维度不匹配问题
- 在KITTI和nuScenes上相比基线模型mAP提升2.1%~3.4%
- 适合自动驾驶中需高精度感知的多模态融合场景
多模态3D目标检测在自动驾驶领域受到广泛关注。然而，现有方法在将3D点云与2D像素粗粒度融合时存在维度不匹配问题，导致融合性能不佳。本文提出FGU3R框架，通过统一3D表征与细粒度融合机制解决该问题，包含两个关键组件：首先，设计了高效的原始点与伪点特征提取器——伪-原始卷积（PRConv），同步调制多模态特征，并基于多模态交互在关键点上聚合不同类型点的特征；其次，提出交叉注意力自适应融合（CAAF），通过变体交叉注意力机制，在细粒度层面自适应融合同质3D RoI特征。两者协同实现统一3D表征下的细粒度融合。在KITTI和nuScenes数据集上的实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
Multimodal 3D object detection has garnered considerable interest in autonomous driving. However, multimodal detectors suffer from dimension mismatches that derive from fusing 3D points with 2D pixels coarsely, which leads to sub-optimal fusion performance. In this paper, we propose a multimodal framework FGU3R to tackle the issue mentioned above via unified 3D representation and fine-grained fusion, which consists of two important components. First, we propose an efficient feature extractor for raw and pseudo points, termed Pseudo-Raw Convolution (PRConv), which modulates multimodal features synchronously and aggregates the features from different types of points on key points based on multimodal interaction. Second, a Cross-Attention Adaptive Fusion (CAAF) is designed to fuse homogeneous 3D RoI (Region of Interest) features adaptively via a cross-attention variant in a fine-grained manner. Together they make fine-grained fusion on unified 3D representation. The experiments conducted on the KITTI and nuScenes show the effectiveness of our proposed method.
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