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

TL;DR

This paper introduces a novel depth estimation method that leverages motion blur, termed light flow, from projected patterns to reconstruct 3D shapes of fast-moving objects, overcoming limitations of traditional structured light techniques.

Contribution

The study proposes using motion blur as a source of depth information, requiring only two projected patterns and analyzing their light flows for accurate 3D shape reconstruction.

Findings

Successfully reconstructed 3D shapes of fast-moving objects.

Demonstrated robustness of the method under motion blur conditions.

Validated the approach with experimental results.

Abstract

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.