# Multi-Spectral Image Stitching via Spatial Graph Reasoning

arXiv: https://arxiv.org/abs/2307.16741 · HTML: https://mlsanity.com/paper/2307.16741/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 59 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 19 of 19)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection](https://mlsanity.com/paper/2203.16220/) (2022)
- [DenseFuse: A Fusion Approach to Infrared and Visible Images](https://mlsanity.com/paper/1804.08361/) (2018)
- [Deep Image Homography Estimation](https://mlsanity.com/paper/1606.03798/) (2016)
- [RFN-Nest: An end-to-end residual fusion network for infrared and visible images](https://mlsanity.com/paper/2103.04286/) (2021)
- [Scene Graph Generation with External Knowledge and Image Reconstruction](https://mlsanity.com/paper/1904.00560/) (2019)
- [Semantic Audio-Visual Navigation](https://mlsanity.com/paper/2012.11583/) (2020)
- [Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model](https://mlsanity.com/paper/1709.03966/) (2017)
- [Unsupervised Deep Image Stitching: Reconstructing Stitched Features to Images](https://mlsanity.com/paper/2106.12859/) (2021)
- [Review on Panoramic Imaging and Its Applications in Scene Understanding](https://mlsanity.com/paper/2205.05570/) (2022)
- [Graph-DETR3D: Rethinking Overlapping Regions for Multi-View 3D Object Detection](https://mlsanity.com/paper/2204.11582/) (2022)
- [Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation](https://mlsanity.com/paper/2107.02524/) (2021)
- [Single-Perspective Warps in Natural Image Stitching](https://mlsanity.com/paper/1802.04645/) (2018)
- [Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation](https://mlsanity.com/paper/2209.05968/) (2022)

## Built on by (most cited first, 4 of 4)

- [Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attacks](https://mlsanity.com/paper/2402.15959/) (2024)
- [Reconstructing the Image Stitching Pipeline: Integrating Fusion and Rectangling into a Unified Inpainting Model](https://mlsanity.com/paper/2404.14951/) (2024)
- [Depth-Supervised Fusion Network for Seamless-Free Image Stitching](https://mlsanity.com/paper/2510.21396/) (2025)
- [SemanticStitch: Enhancing Image Coherence through Foreground-Aware Seam Carving](https://mlsanity.com/paper/2511.12084/) (2025)

## Abstract (arXiv)

Multi-spectral image stitching leverages the complementarity between infrared and visible images to generate a robust and reliable wide field-of-view (FOV) scene. The primary challenge of this task is to explore the relations between multi-spectral images for aligning and integrating multi-view scenes. Capitalizing on the strengths of Graph Convolutional Networks (GCNs) in modeling feature relationships, we propose a spatial graph reasoning based multi-spectral image stitching method that effectively distills the deformation and integration of multi-spectral images across different viewpoints. To accomplish this, we embed multi-scale complementary features from the same view position into a set of nodes. The correspondence across different views is learned through powerful dense feature embeddings, where both inter- and intra-correlations are developed to exploit cross-view matching and enhance inner feature disparity. By introducing long-range coherence along spatial and channel dimensions, the complementarity of pixel relations and channel interdependencies aids in the reconstruction of aligned multi-view features, generating informative and reliable wide FOV scenes. Moreover, we release a challenging dataset named ChaMS, comprising both real-world and synthetic sets with significant parallax, providing a new option for comprehensive evaluation. Extensive experiments demonstrate that our method surpasses the state-of-the-arts.