A Robust Algorithm for Detecting Corners on Triangular Mesh Surfaces

A new algorithm is proposed to detect corners on the triangular mesh surfaces. Based on the minimum principal curvature,a corner feature function at each vertex is evaluated,which accounts for the variance of minimum principal curvature within a local area. Then an iteratively determined threshold of the corner feature function is applied to remove noisy or faint corners. Further,the non-maxima suppression method is employed to extract the distinct corners from local clusters of candidates. To make the corner detection algorithm more robust,the above process is conducted on mesh vertices under different scales to form a multi-scale feature representation at each corner. Experiments on repeated corner detection and registration of partially overlapping surfaces demonstrate the effectiveness and robustness of the proposed algorithm.