Error analysis of 3D shape construction from structured lighting

In this paper, we present a detailed model and analysis of several error sources and thier effects on measuring three-dimensional (3D) surface properties using the structured lighting technique. The analysis is based on a general system configuration and identifies three types of error surces-system modeling error, image processing error and experimental error. Absolute and relative error bounds in obtaining 3D surface orientation and curvature measurements using structured lighting are derived in terms of the system parameters and likely error sources. In addition to the quantization error, other likely error sources in system modeling and experimental setup are also considered. Even though our analysis is on structured lighting, the results are readily applicable to other triangulation-based techniques such as stereopsis. Finally, our analysis focuses on error in inferring surface orientation and principal surface curvature. Such analyses, to our knowledge, have never been attempted before. Image processing Structured light Orientation Curvature Error analysis 1. INTRODUCTION The problem of reconstructing 3D surface structures from their 2D projections is an important research topic in computer vision. Over the past two decades, a variety of techniques have been developed to infer 3D surface structures from 2D images using different imaging devices, shape cues and heuristics. (1-3) These techniques can rely on ambient light reflection (passive sensing) or can employ a light source to actively probe the environment (active sensing). They have also relied on many image shape cues such as stereo disparity, image brightness and surface pattern to recover the depth, orientation and curvature of an imaged surface. To study the feasibility of these 3D shape reconstruction techniques in industrial applications, it is imperative that their accuracy be understood. That is, for each technique, rigid modeling and analysis of the inherent error sources and their effects on 3D shape inference are needed. However, error analysis of all types of sensors used in machine vision is beyond the scope of this paper. Our discussion will be limited to the structured light-sensing technique, which we have some experience with. Thus, the goal of this paper is to identify likely error sources and investigate their effects on computing surface properties using the structured light sensing technique. More precisely, errors in using strucfured lighting to infer surface orientation and principal surface curvatures are analysed. Error bounds are derived in terms of various system parameters and error sources. Simulation was conducted to verify the correctness of the analysis. Structured lighting …