{"ID":2848485,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.25314","arxiv_id":"2510.25314","title":"Seeing Clearly and Deeply: An RGBD Imaging Approach with a Bio-inspired Monocentric Design","abstract":"Achieving high-fidelity, compact RGBD imaging presents a dual challenge: conventional compact optics struggle with RGB sharpness across the entire depth-of-field, while software-only Monocular Depth Estimation (MDE) is an ill-posed problem reliant on unreliable semantic priors. While deep optics with elements like DOEs can encode depth, they introduce trade-offs in fabrication complexity and chromatic aberrations, compromising simplicity. To address this, we first introduce a novel bio-inspired all-spherical monocentric lens, around which we build the Bionic Monocentric Imaging (BMI) framework, a holistic co-design. This optical design naturally encodes depth into its depth-varying Point Spread Functions (PSFs) without requiring complex diffractive or freeform elements. We establish a rigorous physically-based forward model to generate a synthetic dataset by precisely simulating the optical degradation process. This simulation pipeline is co-designed with a dual-head, multi-scale reconstruction network that employs a shared encoder to jointly recover a high-fidelity All-in-Focus (AiF) image and a precise depth map from a single coded capture. Extensive experiments validate the state-of-the-art performance of the proposed framework. In depth estimation, the method attains an Abs Rel of 0.026 and an RMSE of 0.130, markedly outperforming leading software-only approaches and other deep optics systems. For image restoration, the system achieves an SSIM of 0.960 and a perceptual LPIPS score of 0.082, thereby confirming a superior balance between image fidelity and depth accuracy. This study illustrates that the integration of bio-inspired, fully spherical optics with a joint reconstruction algorithm constitutes an effective strategy for addressing the intrinsic challenges in high-performance compact RGBD imaging. Source code will be publicly available at https://github.com/ZongxiYu-ZJU/BMI.","short_abstract":"Achieving high-fidelity, compact RGBD imaging presents a dual challenge: conventional compact optics struggle with RGB sharpness across the entire depth-of-field, while software-only Monocular Depth Estimation (MDE) is an ill-posed problem reliant on unreliable semantic priors. While deep optics with elements like DOEs...","url_abs":"https://arxiv.org/abs/2510.25314","url_pdf":"https://arxiv.org/pdf/2510.25314v1","authors":"[\"Zongxi Yu\",\"Xiaolong Qian\",\"Shaohua Gao\",\"Qi Jiang\",\"Yao Gao\",\"Kailun Yang\",\"Kaiwei Wang\"]","published":"2025-10-29T09:27:38Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.RO\",\"eess.IV\",\"physics.optics\"]","methods":"[]","has_code":false,"code_links":[{"ID":607627,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2848485,"paper_url":"https://arxiv.org/abs/2510.25314","paper_title":"Seeing Clearly and Deeply: An RGBD Imaging Approach with a Bio-inspired Monocentric Design","repo_url":"https://github.com/ZongxiYu-ZJU/BMI","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}