A Reduction Algorithm for the Big Data in 3D Surface Reconstruction

As big data acquisition and storage becomes increasingly affordable, especially in the modern range sensing technology for the scans of complex objects, it is a challenge to reconstruct the surface of 3D geometric model effectively and precisely. In this paper, we describe a reduction method for the big data with noises in the 3D surface reconstruction based on partition of unity, Hermite radial basis functions, and sparse regularization. The proposed method not only provides an approach for pruning some redundant data according to the sparsity, but also contains a good robustness to the noises. This approach can be regarded as one of effective methods for processing big data. Experimental results are also provided.