Focal Split: Untethered Snapshot Depth from Differential Defocus

Junjie Luo*,1, John Mamish*,2, Alan Fu*,1, Thomas Concannon1, Josiah Hester2, Emma Alexander†,3, Qi Guo†,1
1Elmore Family School of Electrical and Computer Engineering, Purdue University
2College of Computing, Georgia Institute of Technology
3McCormick School of Engineering, Northwestern University
IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2025
*Equal Contribution, †ealexander@northwestern.edu, †qiguo@purdue.edu
Paper Supplementary Code arXiv

Abstract

We introduce Focal Split, a handheld, snapshot depth camera with fully onboard power and computing based on depth-from-differential-defocus (DfDD). Focal Split is passive, avoiding power consumption of light sources. Its achromatic optical system simultaneously forms two differentially defocused images of the scene, which can be independently captured using two photosensors in a snapshot. The data processing is based on the DfDD theory, which efficiently computes a depth and a confidence value for each pixel with only 500 floating point operations (FLOPs) per pixel from the camera measurements. We demonstrate a Focal Split prototype, which comprises a handheld custom camera system connected to a Raspberry Pi 5 for real-time data processing. The system consumes 4.9 W and is powered on a 5 V, 10,000 mAh battery. The prototype can measure objects with distances from 0.4 m to 1.2 m, outputting 480 x 360 sparse depth maps at 2.1 frames per second (FPS) using unoptimized Python scripts. Focal Split is DIY friendly. A comprehensive guide to building your own Focal Split depth camera, code, and additional data can be found at https://focal-split.qiguo.org.

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