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所属单位：计算机科学与技术学院/人工智能学院/软件学院

发表刊物：IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

关键字：Mesh denoising patch normal co-filtering kernel low-rank recovery self-similarity

摘要：Mesh denoising is a classical, yet not well-solved problem in digital geometry processing. The challenge arises from noise removal with the minimal disturbance of surface intrinsic properties (e.g., sharp features and shallow details). We propose a new patch normal co-filter (PcFilter) for mesh denoising. It is inspired by the geometry statistics which show that surface patches with similar intrinsic properties exist on the underlying surface of a noisy mesh. We model the PcFilter as a low-rank matrix recovery problem of similar-patch collaboration, aiming at removing different levels of noise, yet preserving various surface features. We generalize our model to pursue the low-rank matrix recovery in the kernel space for handling the nonlinear structure contained in the data. By making use of the block coordinate descent minimization and the specifics of a proximal based coordinate descent method, we optimize the nonlinear and nonconvex objective function efficiently. The detailed quantitative and qualitative results on synthetic and real data show that the PcFilter competes favorably with the state-of-the-art methods in surface accuracy and noise-robustness.

ISSN号：1077-2626

是否译文：否

发表时间：2019-10-01

合写作者：Huang, Jin,黄金泉,Xie, Xingyu,Liu, Ligang,汪俊,Qin, Jing

通讯作者：汪俊,魏明强