This paper focuses on the construction of stronger local features and the effective fusion of image and LiDAR data. We adopt different modalities of LiDAR data to generate richer features and present an adaptive and azimuth-aware network to aggregate local features from image, bird's eye view maps and point cloud. Our network mainly consists of three subnetworks: ground plane estimation network, region proposal network and adaptive fusion network. The ground plane estimation network extracts features of point cloud and predicts the parameters of a plane which are used for generating abundant 3D anchors. The region proposal network generates features of image and bird's eye view maps to output region proposals. To integrate heterogeneous image and point cloud features, the adaptive fusion network explicitly adjusts the intensity of multiple local features and achieves the orientation consistency between image and LiDAR data by introduce an azimuth-aware fusion module. Experiments are conducted on KITTI dataset and the results validate the advantages of our aggregation of multimodal local features and the adaptive fusion network.

中文翻译：

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用于 3D 对象检测的多模态局部特征的自适应和方位角感知融合网络

本文着重于构建更强的局部特征以及图像和激光雷达数据的有效融合。我们采用不同模态的 LiDAR 数据来生成更丰富的特征，并提出一个自适应和方位角感知网络，从图像、鸟瞰图和点云中聚合局部特征。我们的网络主要由三个子网络组成：地平面估计网络、区域提议网络和自适应融合网络。地平面估计网络提取点云的特征并预测用于生成丰富 3D 锚点的平面参数。区域提议网络生成图像和鸟瞰图的特征以输出区域提议。为了整合异构图像和点云特征，自适应融合网络通过引入方位感知融合模块明确调整多个局部特征的强度并实现图像和激光雷达数据之间的方向一致性。在 KITTI 数据集上进行了实验，结果验证了我们聚合多模态局部特征和自适应融合网络的优势。