Abstract: In this paper, we propose a feature-preserving surface reconstruction method from sparse noisy 3D measurements such as range scanning or passive multiview stereo. In contrast to earlier methods, we define a novel type of explicit 3D filter-regularized weighted least squares filter-to characterize the detail features such as surface wrinkles and sharp features. To account for noise, we rasterize input-oriented points into a probabilistic volume (base volume) and then create a guidance volume by Gaussian filtering. Both the base volume and the guidance volume are further filtered by regularized weighted least squares filter to detect and recover detail features. After the two-stage filtering, a global minimal surface is computed by graph cut and meshed as a geometric model. Experimental results on various datasets show that our method is robust to noise, outliers, and missing parts, which makes it more suitable to fit indoor/outdoor multiview stereo data. Unlike other methods, our method can completely recover scene structures and preserve detail features from noisy point samples.