دانلود رایگان مقاله بازنمایی سطح تنک RBF

Abstract

In this paper, we propose a sparse surface representation for arbitrary surface models (point clouds, mesh models, continuous surface models, etc.). We approximate the input surface model with radial basis functions (RBF) whose centers are located on the medial axis of the input object surface. The sparsity of the RBF representation is achieved by solving an L1 optimization problem. Experimental results demonstrate that our method needs much less number of parameters to represent the input surface model with good accuracy. The sparse representation is useful in various applications, such as saving memory space in storing the surface models, and saving time in transmission of the surface models on the internet.

6. Conclusion

In this paper, a sparse surface representation is proposed for arbitrary surface models. The input surface model is approximated with radial basis functions whose centers are located on the medial axis of the input object surface. The sparsity of the RBF representation is computed by solving an L1 optimization problem. Comparisons and experimental results indicate that our method needs much less number of parameters to represent the input surface models. We demonstrate an application of our sparse representation in 3D icon construction which shows that our representation is much more compact than other file formats. As for future work, there are some possible directions to improve our sparse representation. Firstly, radial basis functions in other forms can be investigated to improve the performance of approximation; And other types of basis functions may increase the ability to represent planar models and models with sharp features. Secondly, it will reduce lots of running time if the sparse optimization is combined with some local approximation technique. Furthermore, accelerating the algorithm of our method by GPU implementation is worthy of further investigation.