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

• URL: http://arxiv.org/abs/2209.05968v1
• Date: Tue, 13 Sep 2022 13:01:47 GMT
• Title: Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation
• Authors: Dae-Young Song, Geonsoo Lee, HeeKyung Lee, Gi-Mun Um, and Donghyeon Cho
• Abstract summary: We develop a weakly-supervised learning mechanism to train the stitching model without requiring genuine ground truth images. 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.
• Score: 17.19847723103836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attacks [66.98297584796391]
  Image stitching seamlessly integrates images captured from varying perspectives into a single wide field-of-view image. Given a pair of captured images, subtle perturbations and distortions which go unnoticed by the human visual system tend to attack the correspondence matching. This paper presents the first attempt to improve the robustness of image stitching against adversarial attacks.
  arXiv  Detail & Related papers  (2024-02-25T02:36:33Z)
• Robust Multi-Modal Image Stitching for Improved Scene Understanding [2.0476854378186102]
  We've devised a unique and comprehensive image-stitching pipeline that taps into OpenCV's stitching module. Our approach integrates feature-based matching, transformation estimation, and blending techniques to bring about panoramic views that are of top-tier quality.
  arXiv  Detail & Related papers  (2023-12-28T13:24:48Z)
• SimFIR: A Simple Framework for Fisheye Image Rectification with Self-supervised Representation Learning [105.01294305972037]
  We introduce SimFIR, a framework for fisheye image rectification based on self-supervised representation learning. To learn fine-grained distortion representations, we first split a fisheye image into multiple patches and extract their representations with a Vision Transformer. The transfer performance on the downstream rectification task is remarkably boosted, which verifies the effectiveness of the learned representations.
  arXiv  Detail & Related papers  (2023-08-17T15:20:17Z)
• Practical Wide-Angle Portraits Correction with Deep Structured Models [17.62752136436382]
  This paper introduces the first deep learning based approach to remove perspective distortions from photos. Given a wide-angle portrait as input, we build a cascaded network consisting of a LineNet, a ShapeNet, and a transition module. For the quantitative evaluation, we introduce two novel metrics, line consistency and face congruence.
  arXiv  Detail & Related papers  (2021-04-26T10:47:35Z)
• Stereo Matching by Self-supervision of Multiscopic Vision [65.38359887232025]
  We propose a new self-supervised framework for stereo matching utilizing multiple images captured at aligned camera positions. A cross photometric loss, an uncertainty-aware mutual-supervision loss, and a new smoothness loss are introduced to optimize the network. Our model obtains better disparity maps than previous unsupervised methods on the KITTI dataset.
  arXiv  Detail & Related papers  (2021-04-09T02:58:59Z)
• Learning Edge-Preserved Image Stitching from Large-Baseline Deep Homography [32.28310831466225]
  We propose an image stitching learning framework, which consists of a large-baseline deep homography module and an edge-preserved deformation module. Our method is superior to the existing learning method and shows competitive performance with state-of-the-art traditional methods.
  arXiv  Detail & Related papers  (2020-12-11T08:43:30Z)
• SIR: Self-supervised Image Rectification via Seeing the Same Scene from Multiple Different Lenses [82.56853587380168]
  We propose a novel self-supervised image rectification (SIR) method based on an important insight that the rectified results of distorted images of the same scene from different lens should be the same. We leverage a differentiable warping module to generate the rectified images and re-distorted images from the distortion parameters. Our method achieves comparable or even better performance than the supervised baseline method and representative state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-11-30T08:23:25Z)
• Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement [78.58603635621591]
  Training an unpaired synthetic-to-real translation network in image space is severely under-constrained. We propose a semi-supervised approach that operates on the disentangled shading and albedo layers of the image. Our two-stage pipeline first learns to predict accurate shading in a supervised fashion using physically-based renderings as targets.
  arXiv  Detail & Related papers  (2020-03-27T21:45:41Z)
• Self-Supervised Linear Motion Deblurring [112.75317069916579]
  Deep convolutional neural networks are state-of-the-art for image deblurring. We present a differentiable reblur model for self-supervised motion deblurring. Our experiments demonstrate that self-supervised single image deblurring is really feasible.
  arXiv  Detail & Related papers  (2020-02-10T20:15:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.