用摄像头与雷达融合实现低成本高精度车载分割
CaR1: A Multi-Modal Baseline for BEV Vehicle Segmentation via Camera-Radar Fusion
- 将雷达点云分格编码为结构化鸟瞰图特征
- 在nuScenes上达到57.6的交并比，性能领先
- 适合自动驾驶感知系统研发者参考
摄像头与雷达融合为自动驾驶系统提供了鲁棒且低成本的替代方案。摄像头提供丰富的语义信息但深度不可靠，雷达则提供稀疏但可靠的定位与运动信息。本文提出CaR1，一种基于BEVFusion的新型摄像头-雷达融合架构，通过网格化雷达编码将点云转为结构化鸟瞰图特征，并引入自适应融合机制动态调节传感器贡献权重。在nuScenes数据集上的实验表明，该方法在车辆分割任务中取得57.6的交并比（IoU），性能媲美当前最优方法。代码已公开。
原文摘要 · Abstract (English)
Camera-radar fusion offers a robust and cost-effective alternative to LiDAR-based autonomous driving systems by combining complementary sensing capabilities: cameras provide rich semantic cues but unreliable depth, while radar delivers sparse yet reliable position and motion information. We introduce CaR1, a novel camera-radar fusion architecture for BEV vehicle segmentation. Built upon BEVFusion, our approach incorporates a grid-wise radar encoding that discretizes point clouds into structured BEV features and an adaptive fusion mechanism that dynamically balances sensor contributions. Experiments on nuScenes demonstrate competitive segmentation performance (57.6 IoU), on par with state-of-the-art methods. Code is publicly available \href{https://www.github.com/santimontiel/car1}{online}.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。