Mesh denoising is imperative for improving imperfect surfaces acquired by scanning devices. The main challenge is to faithfully retain geometric features and avoid introducing additional artifacts when removing noise. Unlike the existing mesh denoising techniques that focus only on either the first-order features or high-order differential properties, our approach exploits the synergy when facet normals and quadric surfaces are integrated to recover a piecewise smooth surface. In specific, we vote on surface normal tensors from robust statistics to guide the creation of consistent subneighborhoods subsequently used by moving least squares (MLS). This voting naturally leads to a conceptually simple way that gives a unified mesh-denoising framework for not only handling noise but also enabling the recovering of surfaces with both sharp and small-scale features. The effectiveness of our framework stems from: 1) the multiscale tensor voting that avoids the influence from noise; 2) the effective energy minimization strategy to searching the consistent subneighborhoods; and 3) the piecewise MLS that fully prevents the side effects from different subneighborhoods during surface fitting. Our framework is direct, practical, and easy to understand. Comparisons with the state-of-the-art methods demonstrate its outstanding performance on feature preservation and artifact suppression. Note to Practitioners-Three-dimensional sensing and scanning devices are widely used to capture digital surfaces of real objects and scenes in many scenarios. However, due to occlusion, motion, multiple reflections, and so on, the captured data often suffer from severe contamination with noise, significantly hindering its practical applications. Therefore, it is indispensable to remove noise prior to further processing, which is commonly referred to as mesh denoising. Mesh denoising is a long-standing problem, and remains open in academic as well as industrial applications due to its challenging nature. The state-of-the-art methods either fail to retain most of the original features presented well in the object, or cannot avoid additional artifacts, such as vertex drifts. In contrast, we design a denoising framework aiming at improving the quality of the raw surface by producing a mesh with better perceptual features. The technique developed here can produce high-quality surface data of real objects and scenes, which would facilitate the modeling, reconstruction, and recognition applications in computer-aided design, reverse engineering, 3-D printing, and computer-aided manufacturing.