Sun, YangxingChen, HonghuaQin, JingLi, HongweiWei, MingqiangZong, HuaLee, Jehee and Theobalt, Christian and Wetzstein, Gordon2019-10-142019-10-1420191467-8659https://doi.org/10.1111/cgf.13874https://diglib.eg.org:443/handle/10.1111/cgf13874Semantic surface decomposition (SSD) facilitates various geometry processing and product re-design tasks. Filter-based techniques are meaningful and widely used to achieve the SSD, which however often leads to surface either under-fitting or overfitting. In this paper, we propose a reliable rolling-guided point normal filtering method to decompose textures from a captured point cloud surface. Our method is built on the geometry assumption that 3D surfaces are comprised of an underlying shape (US) and a variety of bump ups and downs (BUDs) on the US. We have three core contributions. First, by considering the BUDs as surface textures, we present a RANSAC-based sub-neighborhood detection scheme to distinguish the US and the textures. Second, to better preserve the US (especially the prominent structures), we introduce a patch shift scheme to estimate the guidance normal for feeding the rolling-guided filter. Third, we formulate a new position updating scheme to alleviate the common uneven distribution of points. Both visual and numerical experiments demonstrate that our method is comparable to state-of-the-art methods in terms of the robustness of texture removal and the effectiveness of the underlying shape preservation.Computing methodologiesPointbased modelsShape analysis10.1111/cgf.13874721-732