← 研踪 PaperTrailCVF · CVPR 2026:cvf:fc98945a8d97171232a86aaa736b10a2 · 记录 #1
Lens Component Deletion based on Differentiable Ray Tracing
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 23:38
摘要
To achieve compactness or cost reduction for optical lens systems, designers typically rely on commercial software to design lens systems independently of post-processing algorithms, leading to excessive dependence on designers' expertise and often requiring significant time. Recently, joint optimization approaches utilizing differentiable ray tracing have emerged, demonstrating significant potential in lens design tasks. However, existing pipelines lack accurate and efficient diffraction modeling capabilities for miniature optical systems. In this work, we propose a novel lens component deletion pipeline for miniature optical systems, which automatically deletes the suitable lens component, and then optimizes both the lens system and the post-processing network to achieve joint aberration correction. Additionally, we introduce a novel metric for evaluating the contribution of each lens component within an optical system, aimed at identifying the lens component that has the least impact on the system. We also develop an efficient differentiable point spread function estimation method based on the Rayleigh-Sommerfeld diffraction model, significantly reducing GPU memory consumption. Our proposed pipeline achieves lens component deletion while maintaining imaging quality comparable to the original lens system, thereby enabling the compactness or cost-effective optimization of miniature optical systems. Project page: https://github.com/WenguanZhang/HappyLens
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记录 #1 · 2026 · 最新观测版本
Lens Component Deletion based on Differentiable Ray Tracing
To achieve compactness or cost reduction for optical lens systems, designers typically rely on commercial software to design lens systems independently of post-processing algorithms, leading to excessive dependence on designers' expertise and often requiring significant time. Recently, joint optimization approaches utilizing differentiable ray tracing have emerged, demonstrating significant potential in lens design tasks. However, existing pipelines lack accurate and efficient diffraction modeling capabilities for miniature optical systems. In this work, we propose a novel lens component deletion pipeline for miniature optical systems, which automatically deletes the suitable lens component, and then optimizes both the lens system and the post-processing network to achieve joint aberration correction. Additionally, we introduce a novel metric for evaluating the contribution of each lens component within an optical system, aimed at identifying the lens component that has the least impact on the system. We also develop an efficient differentiable point spread function estimation method based on the Rayleigh-Sommerfeld diffraction model, significantly reducing GPU memory consumption. Our proposed pipeline achieves lens component deletion while maintaining imaging quality comparable to the original lens system, thereby enabling the compactness or cost-effective optimization of miniature optical systems. Project page: https://github.com/WenguanZhang/HappyLens
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