# Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation

arXiv: https://arxiv.org/abs/2209.05968 · HTML: https://mlsanity.com/paper/2209.05968/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 56 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [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)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [Deep Image Homography Estimation](https://mlsanity.com/paper/1606.03798/) (2016)
- [Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model](https://mlsanity.com/paper/1709.03966/) (2017)
- [Content-Aware Unsupervised Deep Homography Estimation](https://mlsanity.com/paper/1909.05983/) (2019)
- [Deep Homography Estimation for Dynamic Scenes](https://mlsanity.com/paper/2004.02132/) (2020)
- [Unsupervised Deep Image Stitching: Reconstructing Stitched Features to Images](https://mlsanity.com/paper/2106.12859/) (2021)
- [Single-Perspective Warps in Natural Image Stitching](https://mlsanity.com/paper/1802.04645/) (2018)
- [Video Stitching for Linear Camera Arrays](https://mlsanity.com/paper/1907.13622/) (2019)
- [Image Stitching by Line-guided Local Warping with Global Similarity Constraint](https://mlsanity.com/paper/1702.07935/) (2017)
- [Robust image stitching with multiple registrations](https://mlsanity.com/paper/2011.11784/) (2020)
- [Object-centered image stitching](https://mlsanity.com/paper/2011.11789/) (2020)

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

- [Parallax-Tolerant Unsupervised Deep Image Stitching](https://mlsanity.com/paper/2302.08207/) (2023)
- [Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attacks](https://mlsanity.com/paper/2402.15959/) (2024)
- [Multi-Spectral Image Stitching via Spatial Graph Reasoning](https://mlsanity.com/paper/2307.16741/) (2023)
- [Learning Residual Elastic Warps for Image Stitching under Dirichlet Boundary Condition](https://mlsanity.com/paper/2309.01406/) (2023)
- [Implicit Neural Image Stitching](https://mlsanity.com/paper/2309.01409/) (2023)
- [Parallax-Tolerant Image Stitching with Epipolar Displacement Field](https://mlsanity.com/paper/2311.16637/) (2023)
- [Beyond Calibration: Physically Informed Learning for Raw-to-Raw Mapping](https://mlsanity.com/paper/2506.08650/) (2025)
- [Robust Image Stitching with Optimal Plane](https://mlsanity.com/paper/2508.05903/) (2025)
- [ChatStitch: Visualizing Through Structures via Surround-View Unsupervised Deep Image Stitching with Collaborative LLM-Agents](https://mlsanity.com/paper/2503.14948/) (2025)
- [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)

Recently, there has been growing attention on an end-to-end deep learning-based stitching model. However, the most challenging point in deep learning-based stitching is to obtain pairs of input images with a narrow field of view and ground truth images with a wide field of view captured from real-world scenes. To overcome this difficulty, we develop a weakly-supervised learning mechanism to train the stitching model without requiring genuine ground truth images. In addition, we propose a stitching model that takes multiple real-world fisheye images as inputs and creates a 360 output image in an equirectangular projection format. In particular, our model consists of color consistency corrections, warping, and blending, and is trained by perceptual and SSIM losses. The effectiveness of the proposed algorithm is verified on two real-world stitching datasets.