In this paper, we proposed a novel method to estimate the normal information of the unorganized point cloud, which plays an essential part in 3D reconstruction. The original point cloud is firstly divided into cubes with different sizes by the octree method. Then, we fit algebraic sphere in each cube instead of planar surface to improve the accuracy of normal estimation. Finally, the raw normals are refined by a weighting function which increases along with the depth of octree. For evaluation, we compute the intersection angles between the estimated normals and the corresponding groundtruth. Besides, the estimated normals are also plugged into the Poisson surface reconstruction algorithm for intuitive comparison. Experimental results demonstrate the effectiveness of our normal estimating methods. Moreover, the strategy that normal estimation after division saves much more computing time, which promises the efficiency of our method.
['Hongwei Zhao', 'Ding Yuan', 'Hongmei Zhu', 'Jihao Yin']
3-D point cloud normal estimation based on fitting algebraic spheres