# Photometric Depth Super-Resolution

arXiv: https://arxiv.org/abs/1809.10097 · HTML: https://mlsanity.com/paper/1809.10097/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2018.

## Built on by (most cited first, 8 of 8)

- [DeepShadow: Neural Shape from Shadow](https://mlsanity.com/paper/2203.15065/) (2022)
- [FastHuman: Reconstructing High-Quality Clothed Human in Minutes](https://mlsanity.com/paper/2211.14485/) (2022)
- [SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering](https://mlsanity.com/paper/2212.04968/) (2022)
- [Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo](https://mlsanity.com/paper/2404.00098/) (2024)
- [Unpaired Depth Super-Resolution in the Wild](https://mlsanity.com/paper/2105.12038/) (2021)
- [Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields](https://mlsanity.com/paper/2107.06028/) (2021)
- [Can We Use Neural Regularization to Solve Depth Super-Resolution?](https://mlsanity.com/paper/2112.11085/) (2021)
- [Multiple View Performers for Shape Completion](https://mlsanity.com/paper/2209.06291/) (2022)

## Abstract (arXiv)

This study explores the use of photometric techniques (shape-from-shading and uncalibrated photometric stereo) for upsampling the low-resolution depth map from an RGB-D sensor to the higher resolution of the companion RGB image. A single-shot variational approach is first put forward, which is effective as long as the target's reflectance is piecewise-constant. It is then shown that this dependency upon a specific reflectance model can be relaxed by focusing on a specific class of objects (e.g., faces), and delegate reflectance estimation to a deep neural network. A multi-shot strategy based on randomly varying lighting conditions is eventually discussed. It requires no training or prior on the reflectance, yet this comes at the price of a dedicated acquisition setup. Both quantitative and qualitative evaluations illustrate the effectiveness of the proposed methods on synthetic and real-world scenarios.