Disparity map refinement and 3D surface smoothing via directed anisotropic diffusion

We propose a new binocular stereo algorithm and 3D reconstruction method from multiple disparity images. First, we present an accurate binocular stereo algorithm. In our algorithm, we use neither color segmentation nor plane fitting methods, which are common techniques among many sophisticated binocular stereo algorithms. These methods assume that the 3D world consists of a collection of planes and that each segment of a disparity map obeys a plane equation. We exclude these assumptions and introduce a directed anisotropic diffusion technique for refining a disparity map. Second, we show a method to fill some holes in a distance map and smooth the reconstructed 3D surfaces by using another type of anisotropic diffusion technique. The evaluation results on the Middlebury datasets show that our stereo algorithm is competitive with other algorithms that adopt plane fitting methods. We present an experiment that shows the high accuracy of a reconstructed 3D model using our method. The results demonstrate the effectiveness and practicality of our proposed method in a real environment that consists of both curved and planar surfaces.

Research highlights
► The segment constraint is removed to reconstruct general surfaces.
► DAD refines the disparity maps.
► 3D DAD makes the reconstructed 3D surfaces smooth.