Automatic Setting of Optimal Multi-Exposures for High-Quality Structured Light Depth Imaging

Abstract

Structured light 3D cameras have generally been the preferred choice for depth imaging in case high accuracy and precision of point clouds need to be captured at relatively short working distances. However, in environments featuring wide variations in surface reflectivity and ambient lighting, they face difficulty obtaining point clouds that sufficiently cover all the surface areas of interest without partial omissions. This happens not only with a single exposure but even with multiple exposures when a limited range of exposure settings is available. Although automatic multi-exposure settings have been explored for use in structured light 3D cameras to address this issue, there is yet to be a rigorous optimal solution for automatically setting multiple exposures to determine the minimum number of exposure settings while maximally covering the ill-conditioned surface of interest. In the current study, we present a method for optimal multi-exposure settings that automatically determines the minimum number of exposure settings for capturing the maximum number of 3D points from the surface of interest under a limited range of available exposure. The proposed method, which applies to any extremity in surface reflectivity and ambient light variations, is based on finding the minimum number of exposures that enable the identification of exposure-intensity slopes for as many pixels in the surface of interest as possible under projected light while removing the effect of ambient light. The proposed method was experimentally shown to have superior performance based on a case study and a performance comparison with a state-of-the-art multi-exposure setting practice.

Keywords

• Automatic optimal multi-exposure setting
• exposure-intensity slope of a pixel
• point cloud
• structured light depth imaging