aiqbench analysis
This research proposes a novel Bio-inspired Monocentric Imaging (BMI) framework that co-designs optical hardware with deep learning networks to solve the physical bottleneck of monocular depth estimation. By structurally exploiting intrinsic optical aberrations as deterministic carriers for depth encoding, the system achieves high-fidelity 3D sensing across a 120° field of view without external modulators. The approach demonstrates superior performance on standard benchmarks like NYU Depth V2, offering a promising path forward for large-field-of-view autonomous platforms.
Large-FOV RGBD Imaging via Structured PSF Coding in a Bio-inspired Monocentric System
arXiv:2510.25314v2 Announce Type: replace-cross 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 Cram\'er-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{\deg} 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.
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 this https URL.
Accepted to Optics Express (OE). The source code is publicly available at this https URL
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO); Image and Video Processing (eess.IV); Optics (physics.optics)
arXiv:2510.25314 [cs.CV]
(or arXiv:2510.25314v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2510.25314 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 330 words
