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Streamline is one of the most commonly used visualization methods to describe flow field data. With the increase in data scale, the accurate storage of streamlines requires a large amount of storage space. How to store streamlines efficiently is an urgent problem to be solved. Streamline compression is an effective solution. To improve the compression ratio, a compression method with the consideration of the topological relation of the streamlines is proposed in this paper. First, we use a well-designed B-spline curve fitting method to fit the streamlines, during which an intersection test is performed to preserve the topological relation of the streamlines. Then, a lossless compression algorithm is used to compress the fitted streamlines. The experiment illustrates that compared with the existing method, our method can achieve a higher compression ratio, strictly control the compression error and maintain the topological relation of the streamlines. [ABSTRACT FROM AUTHOR]

• We propose an efficient and practical method to do bounded-error remeshing. • If vertices are added into the mesh, the error is easily reduced and finally bounded. • We adaptively refine the mesh to satisfy the error-bounded constraint. • We alternately do remeshing based on an edge length field and adaptively adjust it. We propose an efficient and practically robust method to isotropically remesh an input triangular mesh with bounded approximation error. Our method is based on a key observation, that is, when more uniformly distributed vertices are added into the remeshed mesh, the error-bounded constraint is usually satisfied. Then our algorithm adaptively refines the remeshed mesh to satisfy the error-bounded constraint and avoid heavy computational load. To that end, we present an iterative approach that alternates in each iteration a pass to do an edge-based remeshing using the computed edge length field and a pass to adaptively adjust the edge length field. The robustness of our method is demonstrated by performing tests on complex shapes, as well as models containing sharp features or boundaries. Compared to the state-of-the-art error-bounded methods, our technique is much faster and more practically robust. [ABSTRACT FROM AUTHOR]