Structured light 3-D sensing for scenes with discontinuous reflectivity: error removal based on scene reconstruction and normalization

Abstract

Structured light-based 3-D sensing technique reconstructs the 3-D shape from the disparity given by pixel correspondence of two sensors. However, for scene surface containing discontinuous reflectivity (DR), the captured intensity deviates from its actual value caused by the non-ideal camera point spread function (PSF), thus generating 3-D measurement error. First, we construct the error model of fringe projection profilometry (FPP). From which, we conclude that the DR error of FPP is related to both the camera PSF and the scene reflectivity. The DR error of FPP is hard to be alleviated because of unknown scene reflectivity. Second, we introduce single-pixel imaging (SI) to reconstruct the scene reflectivity and normalize the scene with scene reflectivity "captured" by the projector. From the normalized scene reflectivity, pixel correspondence with error opposite to the original reflectivity is calculated for the DR error removal. Third, we propose an accurate 3-D reconstruction method under discontinuous reflectivity. In this method, pixel correspondence is first established by using FPP, and then refined by using SI with reflectivity normalization. Both the analysis and the measurement accuracy are verified under scenes with different reflectivity distributions in the experiments. As a result, the DR error is effectively alleviated while taking an acceptable measurement time.