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.

该研究提出了一种基于双向融合网络的多传感器融合道路分割方法，该方法利用摄像机成像空间和点云鸟瞰图空间之间的密集空间变换模块以及基于场景的特征融合模块，有效地解决了道路远景损失等问题，并在 KITTI 数据集上取得了有竞争力的结果。