# Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution

arXiv: https://arxiv.org/abs/2011.11865 · HTML: https://mlsanity.com/paper/2011.11865/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data](https://mlsanity.com/paper/2111.08897/) (2021)
- [Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation](https://mlsanity.com/paper/2412.14015/) (2024)
- [Depth Anything with Any Prior](https://mlsanity.com/paper/2505.10565/) (2025)
- [MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices](https://mlsanity.com/paper/2303.01932/) (2023)
- [Multi-sensor large-scale dataset for multi-view 3D reconstruction](https://mlsanity.com/paper/2203.06111/) (2022)
- [Unpaired Depth Super-Resolution in the Wild](https://mlsanity.com/paper/2105.12038/) (2021)
- [2K Retrofit: Entropy-Guided Efficient Sparse Refinement for High-Resolution 3D Geometry Prediction](https://mlsanity.com/paper/2603.19964/) (2026)

## Abstract (arXiv)

Limited by the cost and technology, the resolution of depth map collected by depth camera is often lower than that of its associated RGB camera. Although there have been many researches on RGB image super-resolution (SR), a major problem with depth map super-resolution is that there will be obvious jagged edges and excessive loss of details. To tackle these difficulties, in this work, we propose a multi-scale progressive fusion network for depth map SR, which possess an asymptotic structure to integrate hierarchical features in different domains. Given a low-resolution (LR) depth map and its associated high-resolution (HR) color image, We utilize two different branches to achieve multi-scale feature learning. Next, we propose a step-wise fusion strategy to restore the HR depth map. Finally, a multi-dimensional loss is introduced to constrain clear boundaries and details. Extensive experiments show that our proposed method produces improved results against state-of-the-art methods both qualitatively and quantitatively.