Surface Geometry Processing: An Efficient Normal-based Detail Representation
This addresses efficiency issues in 3D surface processing for applications like texture synthesis and super-resolution, representing an incremental improvement over existing methods.
The paper tackles the problem of high memory and computational costs in processing surface geometry details for high-resolution 3D vision by introducing an efficient normal-based representation, achieving 30 times more input vertices with only 6.5% memory cost and 14.0% running time compared to existing methods.
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.