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RecRecNet: Rectangling Rectified Wide-Angle Images by Thin-Plate Spline Model and DoF-based Curriculum Learning

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Kang Liao, Lang Nie, Chunyu Lin, Zishuo Zheng, Yao Zhao

Computer Vision and Pattern Recognition · Submitted 4 Jan 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experiments show the superiority of our solution over the compared methods on both quantitative and qualitative evaluations.

how it is stated · asserted 1.00 read the record →

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arXiv ID2301.01661 (version 2)
Pith ID7EHXEKJA, Pith's permanent record of this paper and its review
CitationChunyu Lin, Kang Liao, Lang Nie, Yao Zhao, Zishuo Zheng. "RecRecNet: Rectangling Rectified Wide-Angle Images by Thin-Plate Spline Model and DoF-based Curriculum Learning." arXiv:2301.01661 (2023). https://pith.science/paper/7EHXEKJA
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 7,611 input tokens.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.10 · human participants 0.02 · controlled comparison 0.69 · randomized assignment 0.01 · states quantitative result 0.13 · compares with prior results 0.92 · mentions code or data availability 0.99.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The wide-angle lens shows appealing applications in VR technologies, but it introduces severe radial distortion into its captured image. background 1.00 0.07 0.02 asserted 1.00
s1 To recover the realistic scene, previous works devote to rectifying the content of the wide-angle image. background 1.00 0.03 0.02 asserted 1.00
s2 However, such a rectification solution inevitably distorts the image boundary, which changes related geometric distributions and misleads the current vision perception models. gap 0.64 0.08 0.04 asserted 1.00
s3 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). contribution 1.00 0.87 0.23 asserted 0.97
s4 In particular, we propose a thin-plate spline (TPS) module to formulate the non-linear and non-rigid transformation for rectangling images. method 0.82 0.90 0.04 asserted 1.00
s5 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. method 0.99 0.88 0.06 asserted 0.98
s6 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. method 0.89 0.86 0.04 asserted 0.87
s7 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. method 0.99 0.90 0.07 asserted 0.86
s8 Experiments show the superiority of our solution over the compared methods on both quantitative and qualitative evaluations. result 1.00 0.95 0.69 asserted 1.00
s9 The code and dataset are available at https://github.com/KangLiao929/RecRecNet. contribution 0.44 0.46 0.02 asserted 0.96

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.98.

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not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

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.

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2 arXiv papers cite this in 3 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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Contests 1 paper
  • While some works [ref] fill the blank region beyond deformed boundaries using outpainting strategy [ref] , the generated contents are usually fictitious and twist the original semantic characterization.

    Painting Outside the Box: Image Outpainting with GANs · Introduction · contest 0.71

Uses the method or tool of 1 paper
  • Experimental Configuration: We train RecRecNet with an exponentially decaying learning rate with an initial value of $10^-4$ using Adam [ref] .

    Adam: A Method for Stochastic Optimization · Implementation Details · 1.00

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