Efficient depth from defocus imaging for environmental perception

Monocular, stable, low-computation-cost, and high-precision stereo imaging has been a key focus in the fields of robotics, autonomous driving, and machine vision. However, the majority of current stereo imaging research tends to prioritize the stereoscopic effects while neglecting the importance of computational efficiency. This significantly impacts the deployment and application of stereo imaging in industrial settings. In this paper, we propose a stereo imaging scheme based on depth from defocus, integrating the latest advancements in optical systems to overcome the computational efficiency limitations of existing algorithms. By introducing aberration analysis of the optical system, we re-derive the principles of depth from defocus to enhance both the accuracy and range of stereo imaging. Experimental results validate the effectiveness of the proposed method and the accuracy of the precision calculations. Finally, we analyze the impact of optical system parameters on the relevant methods. Theoretical derivations reveal the relationships between spatial resolution, depth resolution, and the range of stereo imaging in depth from defocus approaches, providing theoretical guidance for the design and deployment of such methods in practical application scenarios.