Abstract

We present parallel single-pixel imaging (PSI), a photography technique that captures light transport coefficients and enables the separation of direct and global illumination, to achieve 3D shape reconstruction under strong global illumination. PSI is achieved by extending single-pixel imaging (SI) to modern digital cameras. Each pixel on an imaging sensor is considered an independent unit that can obtain an image using the SI technique. The obtained images characterize the light transport behavior between pixels on the projector and the camera. However, the required number of SI illumination patterns generally becomes unacceptably large in practical situations. We introduce local region extension (LRE) method to accelerate the data acquisition of PSI. LRE perceives that the visible region of each camera pixel accounts for a local region. Thus, the number of detected unknowns is determined by local region area, which is extremely beneficial in terms of data acquisition efficiency. PSI possesses several properties and advantages. For instance, PSI captures the complete light transport coefficients between the projector–camera pair, without making specific assumptions on measured objects and without requiring special hardware and restrictions on the arrangement of the projector–camera pair. The perfect reconstruction property of LRE can be proven mathematically. The acquisition and reconstruction stages are straightforward and easy to implement in the existing projector–camera systems. These properties and advantages make PSI a general and sound theoretical model to decompose direct and global illuminations and perform 3D shape reconstruction under global illumination.

Highlights

  • The appearance of a scene is determined by the 3D geometry structure, the material properties, and the illumination conditions

  • The projector consisted of a lens, a digital micromirror device (DMD), and a light-emitting diode (LED) light source

  • The data captured by PSI are light transport coefficients, which are important in computer vision and graphics

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Summary

Introduction

The appearance of a scene is determined by the 3D geometry structure, the material properties, and the illumination conditions. Light transport equation is an effective way to describe the image formation process in computer vision and graphics. The radiance captured by a camera pixel is calculated by the weighted sum of intensities of every possible position on the light source. These weights, which are termed as light transport coefficients, contain all camera pixel and light source position combinations. Given the huge data volume represented by all camera pixel and light source position combinations, capturing light transport coefficients generally requires a long time. Light transport equation plays an important role in computer vision and graphics, and a large body of work, that is, from image-based relighting (Debevec et al 2000; Masselus et al 2003; Peers et al 2009; Ren et al 2015) to 3D reconstruction

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