# BiFNet: Bidirectional Fusion Network for Road Segmentation

arXiv: https://arxiv.org/abs/2004.08582 · HTML: https://mlsanity.com/paper/2004.08582/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [MonoOcc: Digging into Monocular Semantic Occupancy Prediction](https://mlsanity.com/paper/2403.08766/) (2024)
- [PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration](https://mlsanity.com/paper/2308.04782/) (2023)
- [Unleash the Potential of Image Branch for Cross-modal 3D Object Detection](https://mlsanity.com/paper/2301.09077/) (2023)
- [ROD: RGB-Only Fast and Efficient Off-road Freespace Detection](https://mlsanity.com/paper/2508.08697/) (2025)
- [ABCP: Automatic Block-wise and Channel-wise Network Pruning via Joint Search](https://mlsanity.com/paper/2110.03858/) (2021)
- [MVM3Det: A Novel Method for Multi-view Monocular 3D Detection](https://mlsanity.com/paper/2109.10473/) (2021)
- [COMNet: Co-Occurrent Matching for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2309.16959/) (2023)
- [Towards All-Day Perception for Off-Road Driving: A Large-Scale Multispectral Dataset and Comprehensive Benchmark](https://mlsanity.com/paper/2604.27499/) (2026)

## Abstract (arXiv)

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.