Multi-view image and ToF sensor fusion for dense 3D reconstruction

Abstract

Multi-view stereo methods frequently fail to properly reconstruct 3D scene geometry if visible texture is sparse or the scene exhibits difficult self-occlusions. Time-of-Flight (ToF) depth sensors can provide 3D information regardless of texture but with only limited resolution and accuracy. To find an optimal reconstruction, we propose an integrated multi-view sensor fusion approach that combines information from multiple color cameras and multiple ToF depth sensors. First, multi-view ToF sensor measurements are combined to obtain a coarse but complete model. Then, the initial model is refined by means of a probabilistic multi-view fusion framework, optimizing over an energy function that aggregates ToF depth sensor information with multi-view stereo and silhouette constraints. We obtain high quality dense and detailed 3D models of scenes challenging for stereo alone, while simultaneously reducing complex noise of ToF sensors.