Abstract
Monocular RGBD imaging, also known as simultaneous all-in-focus (AiF) imaging and monocular depth estimation (MDE), represents a significant yet challenging task in computer vision. The crux lies in devising optical coding techniques to maximize the modulation transfer function (MTF) across various depths while minimizing their cross-correlation, all while aligning with the capabilities of image processing algorithms. End-to-end design of optics and algorithms offers a promising avenue towards achieving this holistic objective, but these approaches require solving non-convex inverse problems with millions of parameters. In this paper, we introduce a lattice-focal shape capable of nearly achieving the MTF bound as an initial solution, followed by employing B-spline parameterization for surface geometry representation to reduce the number of optimization variables. Further integration with the Restormer-based neural network, which possesses a global perspective, achieves high-performance RGBD imaging quality. Compared against state-of-the-art monocular RGBD imaging methods, our proposed approach improves the imaging peak signal-to-noise ratio (PSNR) by 3.0 dB and reduces the depth mean absolute error (MAE) by 39%. Experiments in real indoor and outdoor scenes validate the effectiveness of our method. The proposed approach paves the way for the development of monocular RGBD imaging.