Sampled point clouds, particularly with pre-labeled annotations and ground truth metrics, are frequently used in computer graphics and deep learning. In this work, we focus on a fuzzy sampling approach for such point clouds with qualified uniformity properties. After abstracting the uniformity requirements, a novel approach to sampling point clouds from implicitly defined curves/surfaces is proposed. The approach deliberately combines techniques including space isotropic sampling, curvature compensation, and normalized distance blue noise. The experimental results show many kinds of sampled point clouds with uniform visual effects and statistical metrics. Moreover, the comparisons in terms of distance, density, and thickness uniformity with state-of-the-art methods exhibit the approach's advantages. Due to its low cost, ground truth, and annotation easiness features, the method will be applied in many fields.