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Range data obtained from conventional stereo-cameras employing dense stereo matching algorithms typically contain a high amount of noise, especially under poor illumination conditions. Furthermore, lack of reliable depth estimates in low-texture regions can result in poor 3D surface reconstruction. Anisotropic diffusion algorithms have been used recently in stereo matching, depth estimation and 3D surface reconstruction. However, these algorithms typically have long execution times, preventing real-time operation on resource constrained systems and robots. Moreover, most of these techniques suffer from excessive smoothing at depth discontinuities resulting in loss of structure, especially in areas where the 2D image does not provide structural cues to guide the depth diffusion. These algorithms are also unsuitable for diffusion of extremely sparse depth data such as in the case of homogenous surfaces. This paper addresses these issues by novel denoising and diffusion techniques. The results presented demonstrate the run-time efficiency and fidelity of reconstructed depth surfaces.