# RMAFF-PSN: A Residual Multi-Scale Attention Feature Fusion Photometric Stereo Network

arXiv: https://arxiv.org/abs/2404.07766 · HTML: https://mlsanity.com/paper/2404.07766/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [Attentional Feature Fusion](https://mlsanity.com/paper/2009.14082/) (2020)
- [Boundary-Guided Camouflaged Object Detection](https://mlsanity.com/paper/2207.00794/) (2022)
- [PS-FCN: A Flexible Learning Framework for Photometric Stereo](https://mlsanity.com/paper/1807.08696/) (2018)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [CNN-PS: CNN-based Photometric Stereo for General Non-Convex Surfaces](https://mlsanity.com/paper/1808.10093/) (2018)
- [Neural Inverse Rendering for General Reflectance Photometric Stereo](https://mlsanity.com/paper/1802.10328/) (2018)
- [SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes](https://mlsanity.com/paper/1711.07888/) (2017)
- [SPLINE-Net: Sparse Photometric Stereo through Lighting Interpolation and Normal Estimation Networks](https://mlsanity.com/paper/1905.04088/) (2019)
- [Deep Photometric Stereo for Non-Lambertian Surfaces](https://mlsanity.com/paper/2007.13145/) (2020)
- [PX-NET: Simple and Efficient Pixel-Wise Training of Photometric Stereo Networks](https://mlsanity.com/paper/2008.04933/) (2020)
- [Deep Learning Methods for Calibrated Photometric Stereo and Beyond](https://mlsanity.com/paper/2212.08414/) (2022)

## Built on by (most cited first, 1 of 1)

- [MGCR-Net:Multimodal Graph-Conditioned Vision-Language Reconstruction Network for Remote Sensing Change Detection](https://mlsanity.com/paper/2508.01555/) (2025)

## Abstract (arXiv)

Predicting accurate normal maps of objects from two-dimensional images in regions of complex structure and spatial material variations is challenging using photometric stereo methods due to the influence of surface reflection properties caused by variations in object geometry and surface materials. To address this issue, we propose a photometric stereo network called a RMAFF-PSN that uses residual multiscale attentional feature fusion to handle the ``difficult'' regions of the object. Unlike previous approaches that only use stacked convolutional layers to extract deep features from the input image, our method integrates feature information from different resolution stages and scales of the image. This approach preserves more physical information, such as texture and geometry of the object in complex regions, through shallow-deep stage feature extraction, double branching enhancement, and attention optimization. To test the network structure under real-world conditions, we propose a new real dataset called Simple PS data, which contains multiple objects with varying structures and materials. Experimental results on a publicly available benchmark dataset demonstrate that our method outperforms most existing calibrated photometric stereo methods for the same number of input images, especially in the case of highly non-convex object structures. Our method also obtains good results under sparse lighting conditions.