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

arXiv: https://arxiv.org/abs/2510.25314 · HTML: https://mlsanity.com/paper/2510.25314/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 57 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://mlsanity.com/paper/2410.02073/) (2024)
- [Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation](https://mlsanity.com/paper/2404.15506/) (2024)
- [Rethinking Coarse-to-Fine Approach in Single Image Deblurring](https://mlsanity.com/paper/2108.05054/) (2021)
- [Unleashing Text-to-Image Diffusion Models for Visual Perception](https://mlsanity.com/paper/2303.02153/) (2023)
- [Focal Frequency Loss for Image Reconstruction and Synthesis](https://mlsanity.com/paper/2012.12821/) (2020)
- [Deep Optics for Monocular Depth Estimation and 3D Object Detection](https://mlsanity.com/paper/1904.08601/) (2019)
- [Single Image Depth Estimation Trained via Depth from Defocus Cues](https://mlsanity.com/paper/2001.05036/) (2020)
- [ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation](https://mlsanity.com/paper/2403.18807/) (2024)
- [Single-shot Hyperspectral-Depth Imaging with Learned Diffractive Optics](https://mlsanity.com/paper/2009.00463/) (2020)
- [Deep Learning for Omnidirectional Vision: A Survey and New Perspectives](https://mlsanity.com/paper/2205.10468/) (2022)
- [Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring](https://mlsanity.com/paper/2112.05150/) (2021)
- [Robotic Vision for Human-Robot Interaction and Collaboration: A Survey and Systematic Review](https://mlsanity.com/paper/2307.15363/) (2023)
- [Deep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?](https://mlsanity.com/paper/1809.01567/) (2018)
- [Depth Estimation and Image Restoration by Deep Learning from Defocused Images](https://mlsanity.com/paper/2302.10730/) (2023)
- [Exploring Quasi-Global Solutions to Compound Lens Based Computational Imaging Systems](https://mlsanity.com/paper/2404.19201/) (2024)
- [End-to-end Learning for Joint Depth and Image Reconstruction from Diffracted Rotation](https://mlsanity.com/paper/2204.07076/) (2022)
- [DAVIDE: Depth-Aware Video Deblurring](https://mlsanity.com/paper/2409.01274/) (2024)
- [Highly curved image sensors: a practical approach for improved optical performance](https://mlsanity.com/paper/1706.07041/) (2017)
- [Self-Supervised Spatially Variant PSF Estimation for Aberration-Aware Depth-from-Defocus](https://mlsanity.com/paper/2402.18175/) (2024)
- [Learned Off-aperture Encoding for Wide Field-of-view RGBD Imaging](https://mlsanity.com/paper/2507.22523/) (2025)

## Abstract (arXiv)

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 120deg 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.