# Focal Split: Untethered Snapshot Depth from Differential Defocus

arXiv: https://arxiv.org/abs/2504.11202 · HTML: https://mlsanity.com/paper/2504.11202/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [A Survey of Structure from Motion](https://mlsanity.com/paper/1701.08493/) (2017)
- [A Survey on Deep Learning Techniques for Stereo-based Depth Estimation](https://mlsanity.com/paper/2006.02535/) (2020)
- [Towards Real-Time Monocular Depth Estimation for Robotics: A Survey](https://mlsanity.com/paper/2111.08600/) (2021)
- [Depth from Coupled Optical Differentiation](https://mlsanity.com/paper/2409.10725/) (2024)

## Built on by (most cited first, 3 of 3)

- [SpiderCam: Low-Power Snapshot Depth from Differential Defocus](https://mlsanity.com/paper/2603.17910/) (2026)
- [Compact single-shot ranging and near-far imaging using metasurfaces](https://mlsanity.com/paper/2604.10037/) (2026)
- [Depth from Dual Differential Defocus and Stereo Consensus](https://mlsanity.com/paper/2606.02906/) (2026)

## Abstract (arXiv)

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 480×360 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.