# Depth estimation using structured light flow -- analysis of projected pattern flow on an object's surface --

arXiv: https://arxiv.org/abs/1710.00513 · HTML: https://mlsanity.com/paper/1710.00513/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2017.

## Built on by (most cited first, 9 of 9)

- [GLPanoDepth: Global-to-Local Panoramic Depth Estimation](https://mlsanity.com/paper/2202.02796/) (2022)
- [Self-supervised Depth Estimation Leveraging Global Perception and Geometric Smoothness Using On-board Videos](https://mlsanity.com/paper/2106.03505/) (2021)
- [Creating a Forensic Database of Shoeprints from Online Shoe Tread Photos](https://mlsanity.com/paper/2205.02361/) (2022)
- [TIDE: Temporally Incremental Disparity Estimation via Pattern Flow in Structured Light System](https://mlsanity.com/paper/2310.08932/) (2023)
- [Depth Estimation using Weighted-loss and Transfer Learning](https://mlsanity.com/paper/2404.07686/) (2024)
- [Self-supervised Multi-task Learning Framework for Safety and Health-Oriented Connected Driving Environment Perception using Onboard Camera](https://mlsanity.com/paper/2306.11822/) (2023)
- [Online Adaptive Disparity Estimation for Dynamic Scenes in Structured Light Systems](https://mlsanity.com/paper/2310.08934/) (2023)
- [CCDepth: A Lightweight Self-supervised Depth Estimation Network with Enhanced Interpretability](https://mlsanity.com/paper/2409.19933/) (2024)
- [Spatial Polarization Multiplexing: Single-Shot Invisible Shape and Reflectance Recovery](https://mlsanity.com/paper/2504.13177/) (2025)

## Abstract (arXiv)

Shape reconstruction techniques using structured light have been widely researched and developed due to their robustness, high precision, and density. Because the techniques are based on decoding a pattern to find correspondences, it implicitly requires that the projected patterns be clearly captured by an image sensor, i.e., to avoid defocus and motion blur of the projected pattern. Although intensive researches have been conducted for solving defocus blur, few researches for motion blur and only solution is to capture with extremely fast shutter speed. In this paper, unlike the previous approaches, we actively utilize motion blur, which we refer to as a light flow, to estimate depth. Analysis reveals that minimum two light flows, which are retrieved from two projected patterns on the object, are required for depth estimation. To retrieve two light flows at the same time, two sets of parallel line patterns are illuminated from two video projectors and the size of motion blur of each line is precisely measured. By analyzing the light flows, i.e. lengths of the blurs, scene depth information is estimated. In the experiments, 3D shapes of fast moving objects, which are inevitably captured with motion blur, are successfully reconstructed by our technique.