Contour Completion using Deep Structural Priors

• URL: http://arxiv.org/abs/2302.04447v1
• Date: Thu, 9 Feb 2023 05:45:33 GMT
• Title: Contour Completion using Deep Structural Priors
• Authors: Ali Shiraee, Morteza Rezanejad, Mohammad Khodadad, Dirk B. Walther, Hamidreza Mahyar
• Abstract summary: We present a framework that completes disconnected contours and connects fragmented lines and curves. In our framework, we propose a model that does not even need to know which regions of the contour are eliminated. Our work builds a robust framework to achieve contour completion using deep structural priors and extensively investigate how such a model could be implemented.
• Score: 1.7399355670260819
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans can easily perceive illusory contours and complete missing forms in fragmented shapes. This work investigates whether such capability can arise in convolutional neural networks (CNNs) using deep structural priors computed directly from images. In this work, we present a framework that completes disconnected contours and connects fragmented lines and curves. In our framework, we propose a model that does not even need to know which regions of the contour are eliminated. We introduce an iterative process that completes an incomplete image and we propose novel measures that guide this to find regions it needs to complete. Our model trains on a single image and fills in the contours with no additional training data. Our work builds a robust framework to achieve contour completion using deep structural priors and extensively investigate how such a model could be implemented.

Related papers

• Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling [5.584193645582203]
  We introduce deep medial voxels, a semi-implicit representation that faithfully approximates the topological skeleton from imaging volumes. Our reconstruction technique shows potential for both visualization and computer simulations.
  arXiv  Detail & Related papers  (2024-03-18T13:47:18Z)
• RecRecNet: Rectangling Rectified Wide-Angle Images by Thin-Plate Spline Model and DoF-based Curriculum Learning [62.86400614141706]
  We propose a new learning model, i.e., Rectangling Rectification Network (RecRecNet) Our model can flexibly warp the source structure to the target domain and achieves an end-to-end unsupervised deformation. Experiments show the superiority of our solution over the compared methods on both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2023-01-04T15:12:57Z)
• A Structure-Guided Diffusion Model for Large-Hole Image Completion [85.61681358977266]
  We develop a structure-guided diffusion model to fill large holes in images. Our method achieves a superior or comparable visual quality compared to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-18T18:59:01Z)
• Implicit Shape Completion via Adversarial Shape Priors [46.48590354256945]
  We present a novel neural implicit shape method for partial point cloud completion. We combine a conditional Deep-SDF architecture with learned, adversarial shape priors. We train a PointNet++ discriminator that impels the generator to produce plausible, globally consistent reconstructions.
  arXiv  Detail & Related papers  (2022-04-21T12:49:59Z)
• Unsupervised Deep Learning Meets Chan-Vese Model [77.24463525356566]
  We propose an unsupervised image segmentation approach that integrates the Chan-Vese (CV) model with deep neural networks. Our basic idea is to apply a deep neural network that maps the image into a latent space to alleviate the violation of the piecewise constant assumption in image space.
  arXiv  Detail & Related papers  (2022-04-14T13:23:57Z)
• Voxel-based Network for Shape Completion by Leveraging Edge Generation [76.23436070605348]
  We develop a voxel-based network for point cloud completion by leveraging edge generation (VE-PCN) We first embed point clouds into regular voxel grids, and then generate complete objects with the help of the hallucinated shape edges. This decoupled architecture together with a multi-scale grid feature learning is able to generate more realistic on-surface details.
  arXiv  Detail & Related papers  (2021-08-23T05:10:29Z)
• Unsupervised Shape Completion via Deep Prior in the Neural Tangent Kernel Perspective [40.39169145231995]
  We present a novel approach for completing and reconstructing 3D shapes from incomplete scanned data by using deep neural networks. Rather than being trained on supervised completion tasks and applied on a testing shape, the network is optimized from scratch on the single testing shape. The ability to complete missing data by an untrained neural network is usually referred to as the deep prior.
  arXiv  Detail & Related papers  (2021-04-19T02:41:15Z)
• Category Level Object Pose Estimation via Neural Analysis-by-Synthesis [64.14028598360741]
  In this paper we combine a gradient-based fitting procedure with a parametric neural image synthesis module. The image synthesis network is designed to efficiently span the pose configuration space. We experimentally show that the method can recover orientation of objects with high accuracy from 2D images alone.
  arXiv  Detail & Related papers  (2020-08-18T20:30:47Z)
• Neural Subdivision [58.97214948753937]
  This paper introduces Neural Subdivision, a novel framework for data-driven coarseto-fine geometry modeling. We optimize for the same set of network weights across all local mesh patches, thus providing an architecture that is not constrained to a specific input mesh, fixed genus, or category. We demonstrate that even when trained on a single high-resolution mesh our method generates reasonable subdivisions for novel shapes.
  arXiv  Detail & Related papers  (2020-05-04T20:03:21Z)
• Deep Manifold Prior [37.725563645899584]
  We present a prior for manifold structured data, such as surfaces of 3D shapes, where deep neural networks are adopted to reconstruct a target shape using gradient descent. We show that surfaces generated this way are smooth, with limiting behavior characterized by Gaussian processes, and we mathematically derive such properties for fully-connected as well as convolutional networks.
  arXiv  Detail & Related papers  (2020-04-08T20:47:56Z)
• Segmentation and Recovery of Superquadric Models using Convolutional Neural Networks [2.454342521577328]
  We present a (two-stage) approach built around convolutional neural networks (CNNs) In the first stage, our approach uses a Mask RCNN model to identify superquadric-like structures in depth scenes. We are able to describe complex structures with a small number of interpretable parameters.
  arXiv  Detail & Related papers  (2020-01-28T18:17:48Z)

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.