Efficient Colorization of Large-Scale Point Cloud Using Multi-pass Z-Ordering

We present an efficient colorization method for a large scale point cloud using multi-view images. To address the practical issues of noisy camera parameters and color inconsistencies across multi-view images, our method takes an optimization approach for achieving visually pleasing point cloud colorization. We introduce a multi-pass Z-ordering technique that efficiently defines a graph structure to a large-scale and un-ordered set of 3D points, and use the graph structure for optimizing the point colors to be assigned. Our technique is useful for defining minimal but sufficient connectivities among 3D points so that the optimization can exploit the sparsity for efficiently solving the problem. We demonstrate the effectiveness of our method using synthetic datasets and a large-scale real-world data in comparison with other graph construction techniques.