Belief Propagation for Improved Color Assessment in Structured Light

Abstract

Abstract. Single-Shot Structured Light is a well-known method for acquiring 3D surface data of moving scenes with simple and compact hardware setups. Some of the biggest challenges in these systems is their sensitivity to textured scenes, subsurface scattering and low-contrast illumination. Recently, a graphbased method has been proposed that largely eliminates these shortcomings. A key step in the graph-based pattern decoding algorithm is the estimation of color of local image regions which correspond to the vertex colors of the graph. In this work we propose a new method for estimating the color of a vertex based on belief propagation (BP). The BP framework allows the explicit inclusion of cues from neigboring vertices in the color estimation. This is especially beneficial for low-contrast input images. The augmented method is evaluated using typical lowquality real-world test sequences of the interior of a pig stomach. We demonstrate a significant improvement in robustness. The number of 3D data points generated increases by 30 to 50 percent over the plain decoding.