Passive Ranging via Differential Defocus

Abstract: Depth sensors are essential wherever robots, vehicles, and wearables must understand 3D scenes. As these applications spread to ever smaller platforms, like drones, underwater vehicles, mobile and wearable devices, building depth sensors that are more compact and more power-efficient has become a priority in both academia and industry. Today, two approaches dominate: active systems such as LiDAR, which spend power projecting light onto the scene, and passive triangulation methods such as stereo, which require a wide separation between viewpoints and substantial computation. Neither shrinks gracefully. This talk presents an alternative: differential defocus, a passive sensing principle that recovers distance from how defocus blur changes between slightly different images, turning depth estimation into a simple, closed-form calculation at each pixel. The talk examines this idea through three systems: snapshot depth sensing, low-computation depth sensing, and accurate triangulation-based depth sensing. Each project addresses a different requirement of practical depth sensing. For snapshot depth sensing, Focal Split (CVPR 2025), a handheld depth-from-differential-defocus camera with fully onboard power and computing, captures two differently focused images in a single exposure and converts them into depth maps in real time on a Raspberry Pi at under 5 W. For low-computation depth sensing, depth from coupled optical differentiation (IJCV) provides the underlying theory: each pixel's distance can be read off from how the image responds to a small, coupled adjustment of the camera's optics, through a closed-form formula that costs only a few hundred operations per pixel, is markedly more robust to noise than earlier defocus-based methods, and holds regardless of the aperture design. For accurate triangulation-based depth sensing, D³S, the most recent work in this line, computes distance independently from two physical cues, defocus and stereo, and keeps only the estimates on which both agree. This consensus yields depth maps with 1-cm mean error over a 0.3-1.64 m range from a prototype whose two lenses sit just 3.84 mm apart, a working range that previously required camera separations roughly ten times larger. Together, these results point to passive, physics-grounded depth sensing as a practical building block for platforms too small or too power-constrained for today's depth cameras. Bio: Junjie Luo is a graduating Ph.D. on the job market in the Elmore Family School of Electrical and Computer Engineering at Purdue University, advised by Prof. Qi Guo. He develops passive depth sensors that combine unconventional optics with closed-form, physics-based algorithms to recover accurate 3D information with compact, low-power cameras. His first-authored research includes Focal Split, a handheld snapshot depth camera presented at CVPR 2025; depth from coupled optical differentiation, published in the International Journal of Computer Vision; and a compact metasurface system for simultaneous near-field ranging and far-field imaging, developed under a Navy STTR project and presented at SPIE Defense + Security 2026. He also works closely with Professor Emma Alexander on computational imaging research. Before joining Purdue, he earned a BS in Software Engineering from Sun Yat-sen University. More broadly, he is interested in the co-design of optics and algorithms for reliable 3D imaging with compact hardware. Homepage: https://luo-jun-jie.github.io/ Sponsor: The VASC seminar is generously sponsored by HeyGen, an all-in-one AI-powered video generation platform that leverages advances in computer vision, generative modeling, and multimodal learning to make high-quality video creation both scalable and accessible.