Cam2BEV

Why we included this project

Teams building perception stacks for automated driving will find a compact, well-documented reference here. Cam2BEV fuses images from several vehicle-mounted cameras into a single semantically segmented bird's eye view, and it fixes the distortion that plain inverse perspective mapping leaves on three-dimensional objects like cars and pedestrians. It also fills in occluded areas, which is what you want when the goal is a complete 360-degree surround view rather than a flat ground-plane projection. The paper behind it describes a sim2real training approach, so it is a useful example of how synthetic data can train a real-world perception model. This is research-grade code, not a turnkey product, but it gives a clear starting point for anyone implementing BEV fusion and segmentation in their own stack.