论文标题
BIFNET：双向融合网络
BiFNet: Bidirectional Fusion Network for Road Segmentation
论文作者
论文摘要
基于多传感器融合的道路细分在智能驾驶系统中起着重要作用，因为它提供了可驱动的区域。现有的主流融合方法主要是在图像空间域中具有融合，这会导致道路的透视压望并损害了遥远的道路的性能。考虑到LiDAR的鸟类视图（BEV）仍然是水平面中的空间结构，本文提出了双向融合网络（BIFNET），以融合点云的图像和BEV。该网络由两个模块组成：1）密集的空间转换模块，该模块解决了相机图像空间和BEV空间之间的相互转换。 2）基于上下文的特征融合模块，该模块基于相应功能的场景融合不同的传感器信息。此方法在KITTI数据集上实现了竞争结果。
Multi-sensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. The existing mainstream fusion method is mainly to feature fusion in the image space domain which causes the perspective compression of the road and damages the performance of the distant road. Considering the bird's eye views(BEV) of the LiDAR remains the space structure in horizontal plane, this paper proposes a bidirectional fusion network(BiFNet) to fuse the image and BEV of the point cloud. The network consists of two modules: 1) Dense space transformation module, which solves the mutual conversion between camera image space and BEV space. 2) Context-based feature fusion module, which fuses the different sensors information based on the scenes from corresponding features.This method has achieved competitive results on KITTI dataset.
