Ridge-valley features are important elements of a model. To recognize these features from point cloud, this paper introduces a new criterion named Extremal Point Distance (EPD) to greatly reduce the number of potential feature points and locate feature position more accurately. On this basis, a feature enhancement algorithm is proposed. The algorithm adjusts the coordinates of feature regions by minimizing a linear objective function consisting of expected position and normal, which can ensure the accurate sampling of feature position. We also present a parameterization method to eliminate the lateral sliding of feature points and reduce the number of unknowns in the objective function. Since the EPD criterion only depends on the changing trend, rather than the absolute value of the curvature, our algorithm can infer the expected position and normal with a large neighborhood radius, which makes it robust to noise. Experiments show that our algorithm can adjust the feature amplitude and sharpness freely, and achieve satisfactory results in feature recognition, feature enhancement and sharp feature restoration.

岭谷特征是模型的重要元素。为了从点云中识别这些特征，本文引入了一种名为极值点距离（EPD）的新准则，以大大减少潜在特征点的数量并更准确地定位特征位置。在此基础上，提出了一种特征增强算法。该算法通过最小化由期望位置和法线组成的线性目标函数来调整特征区域的坐标，保证特征位置的准确采样。我们还提出了一种参数化方法来消除特征点的横向滑动并减少目标函数中未知数的数量。由于 EPD 准则只取决于变化趋势，而不是曲率的绝对值，我们的算法可以推断出具有大邻域半径的​​预期位置和法线，这使其对噪声具有鲁棒性。实验表明，我们的算法可以自由调整特征幅度和锐度，在特征识别、特征增强和锐利特征恢复方面取得了令人满意的效果。