# RecRecNet: Rectangling Rectified Wide-Angle Images by Thin-Plate Spline Model and DoF-based Curriculum Learning arXiv: https://arxiv.org/abs/2301.01661 ยท HTML: https://mlsanity.com/paper/2301.01661/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 35 of its 66 references that are in the map. - First posted on arXiv 4 Jan 2023. ## Builds on (most cited first, 30 of 35) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [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) - [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017) - [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017) - [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020) - [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015) - [How Much Position Information Do Convolutional Neural Networks Encode?](https://mlsanity.com/paper/2001.08248/) (2020) - [On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location](https://mlsanity.com/paper/2003.07064/) (2020) - [Convolutional neural network architecture for geometric matching](https://mlsanity.com/paper/1703.05593/) (2017) - [Deep Image Homography Estimation](https://mlsanity.com/paper/1606.03798/) (2016) - [CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images](https://mlsanity.com/paper/1808.01097/) (2018) - [Learning to Execute](https://mlsanity.com/paper/1410.4615/) (2014) - [Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes](https://mlsanity.com/paper/1707.09465/) (2017) - [Positional Encoding as Spatial Inductive Bias in GANs](https://mlsanity.com/paper/2012.05217/) (2020) - [Curriculum Learning of Multiple Tasks](https://mlsanity.com/paper/1412.1353/) (2014) - [Boundless: Generative Adversarial Networks for Image Extension](https://mlsanity.com/paper/1908.07007/) (2019) - [How hard can it be? Estimating the difficulty of visual search in an image](https://mlsanity.com/paper/1705.08280/) (2017) - [Curriculum Dropout](https://mlsanity.com/paper/1703.06229/) (2017) - [Capturing Omni-Range Context for Omnidirectional Segmentation](https://mlsanity.com/paper/2103.05687/) (2021) - [Review on Panoramic Imaging and Its Applications in Scene Understanding](https://mlsanity.com/paper/2205.05570/) (2022) - [Mind the Pad -- CNNs can Develop Blind Spots](https://mlsanity.com/paper/2010.02178/) (2020) - [Curriculum Self-Paced Learning for Cross-Domain Object Detection](https://mlsanity.com/paper/1911.06849/) (2019) - [FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification](https://mlsanity.com/paper/1804.04784/) (2018) - [Painting Outside the Box: Image Outpainting with GANs](https://mlsanity.com/paper/1808.08483/) (2018) - [Blind Geometric Distortion Correction on Images Through Deep Learning](https://mlsanity.com/paper/1909.03459/) (2019) - [Learning to Calibrate Straight Lines for Fisheye Image Rectification](https://mlsanity.com/paper/1904.09856/) (2019) - [Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)](https://mlsanity.com/paper/1910.08967/) (2019) - [Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow](https://mlsanity.com/paper/2103.16026/) (2021) ## Built on by (most cited first, 8 of 8) - [RecDiffusion: Rectangling for Image Stitching with Diffusion Models](https://mlsanity.com/paper/2403.19164/) (2024) - [Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler](https://mlsanity.com/paper/2409.17555/) (2024) - [MOWA: Multiple-in-One Image Warping Model](https://mlsanity.com/paper/2404.10716/) (2024) - [FishDreamer: Towards Fisheye Semantic Completion via Unified Image Outpainting and Segmentation](https://mlsanity.com/paper/2303.13842/) (2023) - [Semi-Supervised Coupled Thin-Plate Spline Model for Rotation Correction and Beyond](https://mlsanity.com/paper/2401.13432/) (2024) - [TSCL:Multi-party loss Balancing scheme for deep learning Image steganography based on Curriculum learning](https://mlsanity.com/paper/2504.18348/) (2025) - [Rectification Reimagined: A Unified Mamba Model for Image Correction and Rectangling with Prompts](https://mlsanity.com/paper/2512.18718/) (2025) - [UniStitch: Unifying Semantic and Geometric Features for Image Stitching](https://mlsanity.com/paper/2603.10568/) (2026) ## Abstract (arXiv) The wide-angle lens shows appealing applications in VR technologies, but it introduces severe radial distortion into its captured image. To recover the realistic scene, previous works devote to rectifying the content of the wide-angle image. However, such a rectification solution inevitably distorts the image boundary, which changes related geometric distributions and misleads the current vision perception models. In this work, we explore constructing a win-win representation on both content and boundary by contributing a new learning model, i.e., Rectangling Rectification Network (RecRecNet). In particular, we propose a thin-plate spline (TPS) module to formulate the non-linear and non-rigid transformation for rectangling images. By learning the control points on the rectified image, our model can flexibly warp the source structure to the target domain and achieves an end-to-end unsupervised deformation. To relieve the complexity of structure approximation, we then inspire our RecRecNet to learn the gradual deformation rules with a DoF (Degree of Freedom)-based curriculum learning. By increasing the DoF in each curriculum stage, namely, from similarity transformation (4-DoF) to homography transformation (8-DoF), the network is capable of investigating more detailed deformations, offering fast convergence on the final rectangling task. Experiments show the superiority of our solution over the compared methods on both quantitative and qualitative evaluations. The code and dataset are available at https://github.com/KangLiao929/RecRecNet.