# 3D Appearance Super-Resolution with Deep Learning

arXiv: https://arxiv.org/abs/1906.00925 · HTML: https://mlsanity.com/paper/1906.00925/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

## Built on by (most cited first, 11 of 11)

- [Attention-based Multi-Reference Learning for Image Super-Resolution](https://mlsanity.com/paper/2108.13697/) (2021)
- [Super-resolution 3D Human Shape from a Single Low-Resolution Image](https://mlsanity.com/paper/2208.10738/) (2022)
- [Deep Deformation Detail Synthesis for Thin Shell Models](https://mlsanity.com/paper/2102.11541/) (2021)
- [PBR-SR: Mesh PBR Texture Super Resolution from 2D Image Priors](https://mlsanity.com/paper/2506.02846/) (2025)
- [Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution](https://mlsanity.com/paper/2207.08601/) (2022)
- [Fast Monte Carlo Rendering via Multi-Resolution Sampling](https://mlsanity.com/paper/2106.12802/) (2021)
- [Super-Resolution Appearance Transfer for 4D Human Performances](https://mlsanity.com/paper/2108.13739/) (2021)
- [Improving Multi-View Stereo via Super-Resolution](https://mlsanity.com/paper/2107.13261/) (2021)
- [Auxiliary Features-Guided Super Resolution for Monte Carlo Rendering](https://mlsanity.com/paper/2310.13235/) (2023)
- [Advances in Neural 3D Mesh Texturing: A Survey](https://mlsanity.com/paper/2606.00137/) (2026)
- [Texture++: Elevating 3D Asset Texture Resolution with a Region-Aware Diffusion Model](https://mlsanity.com/paper/2607.21504/) (2026)

## Abstract (arXiv)

We tackle the problem of retrieving high-resolution (HR) texture maps of objects that are captured from multiple view points. In the multi-view case, model-based super-resolution (SR) methods have been recently proved to recover high quality texture maps. On the other hand, the advent of deep learning-based methods has already a significant impact on the problem of video and image SR. Yet, a deep learning-based approach to super-resolve the appearance of 3D objects is still missing. The main limitation of exploiting the power of deep learning techniques in the multi-view case is the lack of data. We introduce a 3D appearance SR (3DASR) dataset based on the existing ETH3D [42], SyB3R [31], MiddleBury, and our Collection of 3D scenes from TUM [21], Fountain [51] and Relief [53]. We provide the high- and low-resolution texture maps, the 3D geometric model, images and projection matrices. We exploit the power of 2D learning-based SR methods and design networks suitable for the 3D multi-view case. We incorporate the geometric information by introducing normal maps and further improve the learning process. Experimental results demonstrate that our proposed networks successfully incorporate the 3D geometric information and super-resolve the texture maps.