LAUSR

Structured light depth imaging offers a high precision of 3D measurement for applications to industrial modeling, inspection, reproduction and archiving. Despite recent progress, however, structured light depth imaging has yet to settle the trade-off between the number of captured 3D points and that of unwanted outliers, in particular, when a low reflectance surface is mixed with projector and camera shades. This paper presents an approach to solving such trade-off by accurately mapping projector and camera shades under the presence of a low reflectance surface. Unlike conventional approaches relying mostly on pattern intensity for detecting shades, the proposed approach first identifies stripe boundaries legitimate in terms of the projected pattern sequence. Based on the list of legitimate boundaries identified, then, the gaps in pixel and boundary addresses are analyzed to define the respective projector and camera shades, possibly, in the presence of address juxtaposition. Experimental results show that the proposed approach shows superior performance in shade detection as well as outlier removal compared to the state-of-the-art approaches, maximizing the number of captured 3D points while minimizing that of unwanted outliers.