用雷达回波分布模型提升相机-雷达3D目标检测精度
RICCARDO: Radar Hit Prediction and Convolution for Camera-Radar 3D Object Detection
- 基于单目检测结果预测物体的雷达回波分布
- 用预测分布作为卷积核匹配实际雷达点，生成位置得分
- 在nuScenes上达到当前最优性能，适合自动驾驶感知研究
雷达回波来自物体边界及内部点，其分布复杂，受物体类别、尺寸和朝向影响。现有相机-雷达融合方法通过黑箱神经网络隐式处理这一特性。本文提出显式建模雷达回波分布的方法：首先，基于单目检测获取的物体属性预测雷达回波分布；其次，将预测分布作为卷积核，在单目检测邻近区域匹配实际雷达点，生成位置匹配分数；最后，融合上下文信息与卷积检测器，优化匹配分数。该方法在nuScenes数据集上实现了当前最优的相机-雷达3D目标检测性能。代码已开源。
原文摘要 · Abstract (English)
Radar hits reflect from points on both the boundary and internal to object outlines. This results in a complex distribution of radar hits that depends on factors including object category, size, and orientation. Current radar-camera fusion methods implicitly account for this with a black-box neural network. In this paper, we explicitly utilize a radar hit distribution model to assist fusion. First, we build a model to predict radar hit distributions conditioned on object properties obtained from a monocular detector. Second, we use the predicted distribution as a kernel to match actual measured radar points in the neighborhood of the monocular detections, generating matching scores at nearby positions. Finally, a fusion stage combines context with the kernel detector to refine the matching scores. Our method achieves the state-of-the-art radar-camera detection performance on nuScenes. Our source code is available at https://github.com/longyunf/riccardo.
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