# Surface Geometry Processing: An Efficient Normal-based Detail Representation

arXiv: https://arxiv.org/abs/2307.07945 · HTML: https://mlsanity.com/paper/2307.07945/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 54 references that are in the map.
- First posted on arXiv 16 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions](https://mlsanity.com/paper/1603.08182/) (2016)
- [PU-Net: Point Cloud Upsampling Network](https://mlsanity.com/paper/1801.06761/) (2018)
- [Tex2Shape: Detailed Full Human Body Geometry From a Single Image](https://mlsanity.com/paper/1904.08645/) (2019)
- [Variational Autoencoders for Deforming 3D Mesh Models](https://mlsanity.com/paper/1709.04307/) (2017)
- [Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials](https://mlsanity.com/paper/2001.06659/) (2020)
- [Deep Geometric Texture Synthesis](https://mlsanity.com/paper/2007.00074/) (2020)
- [Deep Photometric Stereo for Non-Lambertian Surfaces](https://mlsanity.com/paper/2007.13145/) (2020)
- [Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene](https://mlsanity.com/paper/1911.09092/) (2019)
- [Neural Voxel Renderer: Learning an Accurate and Controllable Rendering Tool](https://mlsanity.com/paper/1912.04591/) (2019)
- [A compact formula for the derivative of a 3-D rotation in exponential coordinates](https://mlsanity.com/paper/1312.0788/) (2013)
- [Face De-occlusion using 3D Morphable Model and Generative Adversarial Network](https://mlsanity.com/paper/1904.06109/) (2019)
- [DNF-Net: a Deep Normal Filtering Network for Mesh Denoising](https://mlsanity.com/paper/2006.15510/) (2020)

## Abstract (arXiv)

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.