Large-FOV RGBD Imaging via Structured PSF Coding in a Bio-inspired Monocentric System

Abstract

High-fidelity large-field-of-view (LFOV) 3D sensing, essential for autonomous platforms, is hindered by the coupling of anisotropic off-axis aberrations and the ill-posed nature of monocular depth estimation. To address this fundamental physical bottleneck, we propose the Bio-inspired Monocentric Imaging (BMI) framework, a holistic co-design integrating a monocentric optical topology with a physics-aware reconstruction network. By pairing a concentric spherical lens with a hemispherical sensor, our system structurally eliminates coma, astigmatism, and field curvature. Instead of relying on external modulators or hardware redundancy, we exploit the intrinsic aberration as a deterministic, radially symmetric carrier for depth encoding. Theoretical analysis via the Cramér-Rao Lower Bound confirms that this topology maintains superior depth sensitivity across the entire FOV. To decode these optical cues, our framework utilizes a dual-head network to jointly recover high-fidelity All-in-Focus images and dense metric depth maps from single-shot captures. The efficacy of this co-design is evaluated through physically-based simulations of field-dependent PSFs across the full optical FOV. On NYU Depth V2, our system achieves a superior balance of image fidelity (31.15dB PSNR) and depth precision (0.161m RMSE). It further maintains consistent reconstruction under spatially varying PSFs sampled across a 120° optical FOV, mitigating the peripheral degradation observed in conventional wide-field designs. The source code is publicly available at https://github.com/ZongxiYu-ZJU/BMI.