Stereo Vision: Camera Agnostic Asymmetric Stereo

This paper presents a real-time AI-based stereo vision system for automotive applications. The model is capable of handling cameras with different lens types such as pinhole and fisheye, as well as arbitrary camera poses. A coarse to fine model is implemented, which can provide depth information at various resolutions. The cost volume is generated using spherical/depth sweeping method and based on the intrinsic and extrinsic parameters. The model is trained and tested for different camera arrangements using synthetic datasets generated in CARLA. The results show that the proposed model is camera-agnostic, allowing models trained on specific camera pairs to transfer to other setups without retraining. Thus possible misalignment can be compensated by recalibrating extrinsic parameters and modifying the cost volume creation. This system is well-suited for modern vehicles equipped with multiple different cameras, and offers a robust depth estimation for applications like low speed maneuver and parking assistance.