ProAlignNet : Unsupervised Learning for Progressively Aligning Noisy Contours

• URL: http://arxiv.org/abs/2005.11546v1
• Date: Sat, 23 May 2020 14:56:14 GMT
• Title: ProAlignNet : Unsupervised Learning for Progressively Aligning Noisy Contours
• Authors: VSR Veeravasarapu, Abhishek Goel, Deepak Mittal, Maneesh Singh
• Abstract summary: "ProAlignNet" accounts for large scale misalignments and complex transformations between the contour shapes. It learns by training with a novel loss function which is derived an upperbound of a proximity-sensitive and local shape-dependent similarity metric. In two real-world applications, the proposed models consistently perform superior to state-of-the-art methods.
• Score: 12.791313859673187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contour shape alignment is a fundamental but challenging problem in computer vision, especially when the observations are partial, noisy, and largely misaligned. Recent ConvNet-based architectures that were proposed to align image structures tend to fail with contour representation of shapes, mostly due to the use of proximity-insensitive pixel-wise similarity measures as loss functions in their training processes. This work presents a novel ConvNet, "ProAlignNet" that accounts for large scale misalignments and complex transformations between the contour shapes. It infers the warp parameters in a multi-scale fashion with progressively increasing complex transformations over increasing scales. It learns --without supervision-- to align contours, agnostic to noise and missing parts, by training with a novel loss function which is derived an upperbound of a proximity-sensitive and local shape-dependent similarity metric that uses classical Morphological Chamfer Distance Transform. We evaluate the reliability of these proposals on a simulated MNIST noisy contours dataset via some basic sanity check experiments. Next, we demonstrate the effectiveness of the proposed models in two real-world applications of (i) aligning geo-parcel data to aerial image maps and (ii) refining coarsely annotated segmentation labels. In both applications, the proposed models consistently perform superior to state-of-the-art methods.

Related papers

• WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration [68.25711405944239]
  Deep image registration has demonstrated exceptional accuracy and fast inference. Recent advances have adopted either multiple cascades or pyramid architectures to estimate dense deformation fields in a coarse-to-fine manner. We introduce a model-driven WiNet that incrementally estimates scale-wise wavelet coefficients for the displacement/velocity field across various scales.
  arXiv  Detail & Related papers  (2024-07-18T11:51:01Z)
• Mesh Denoising Transformer [104.5404564075393]
  Mesh denoising is aimed at removing noise from input meshes while preserving their feature structures. SurfaceFormer is a pioneering Transformer-based mesh denoising framework. New representation known as Local Surface Descriptor captures local geometric intricacies. Denoising Transformer module receives the multimodal information and achieves efficient global feature aggregation.
  arXiv  Detail & Related papers  (2024-05-10T15:27:43Z)
• Deformation-Guided Unsupervised Non-Rigid Shape Matching [7.327850781641328]
  We present an unsupervised data-driven approach for non-rigid shape matching. Our approach is particularly robust when matching digitized shapes using 3D scanners.
  arXiv  Detail & Related papers  (2023-11-27T09:55:55Z)
• Non-Rigid Shape Registration via Deep Functional Maps Prior [1.9249120068573227]
  We propose a learning-based framework for non-rigid shape registration without correspondence supervision. We deform source mesh towards the target point cloud, guided by correspondences induced by high-dimensional embeddings. Our pipeline achieves state-of-the-art results on several benchmarks of non-rigid point cloud matching.
  arXiv  Detail & Related papers  (2023-11-08T06:52:57Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis [66.49788145564004]
  We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology. Our methods perform favorably against the current state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-12-17T15:05:25Z)
• Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transport [80.64516377977183]
  Shape matching has been a long-studied problem for the computer graphics and vision community. We investigate a hierarchical learning design, to which we incorporate local patch-level information and global shape-level structures. We propose a novel optimal transport solver by recurrently updating features on non-confident nodes to learn globally consistent correspondences between the shapes.
  arXiv  Detail & Related papers  (2022-02-03T11:41:46Z)
• Sign-Agnostic CONet: Learning Implicit Surface Reconstructions by Sign-Agnostic Optimization of Convolutional Occupancy Networks [39.65056638604885]
  We learn implicit surface reconstruction by sign-agnostic optimization of convolutional occupancy networks. We show this goal can be effectively achieved by a simple yet effective design.
  arXiv  Detail & Related papers  (2021-05-08T03:35:32Z)
• Learning Graph-Convolutional Representations for Point Cloud Denoising [31.557988478764997]
  We propose a deep neural network that can deal with the permutation-invariance problem encountered by learning-based point cloud processing methods. The network is fully-convolutional and can build complex hierarchies of features by dynamically constructing neighborhood graphs. It is especially robust both at high noise levels and in presence of structured noise such as the one encountered in real LiDAR scans.
  arXiv  Detail & Related papers  (2020-07-06T08:11:28Z)
• Deep Semantic Matching with Foreground Detection and Cycle-Consistency [103.22976097225457]
  We address weakly supervised semantic matching based on a deep network. We explicitly estimate the foreground regions to suppress the effect of background clutter. We develop cycle-consistent losses to enforce the predicted transformations across multiple images to be geometrically plausible and consistent.
  arXiv  Detail & Related papers  (2020-03-31T22:38:09Z)
• Multistage Model for Robust Face Alignment Using Deep Neural Networks [8.504539228134082]
  A multistage model is proposed which takes advantage of spatial transformer networks, hourglass networks and exemplar-based shape constraints. Experiments are performed to demonstrate the superior performance of the proposed method over other state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-02-04T01:13:58Z)

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.