Surface Geometry Processing: An Efficient Normal-based Detail
  Representation
Wuyuan Xie Miaohui Wang Di Lin Boxin Shi Jianmin Jiang
With the rapid development of high-resolution 3D vision applications, the
traditional way of manipulating surface detail requires considerable memory and
computing time. To address these problems, we introduce an efficient surface
detail processing framework in 2D normal domain, which extracts new normal
feature representations as the carrier of micro geometry structures that are
illustrated both theoretically and empirically in this article. Compared with
the existing state of the arts, we verify and demonstrate that the proposed
normal-based representation has three important properties, including detail
separability, detail transferability and detail idempotence. Finally, three new
schemes are further designed for geometric surface detail processing
applications, including geometric texture synthesis, geometry detail transfer,
and 3D surface super-resolution. Theoretical analysis and experimental results
on the latest benchmark dataset verify the effectiveness and versatility of our
normal-based representation, which accepts 30 times of the input surface
vertices but at the same time only takes 6.5% memory cost and 14.0% running
time in comparison with existing competing algorithms.