GP-Aligner: Unsupervised Non-rigid Groupwise Point Set Registration Based On Optimized Group Latent Descriptor

• URL: http://arxiv.org/abs/2007.12979v1
• Date: Sat, 25 Jul 2020 17:09:53 GMT
• Title: GP-Aligner: Unsupervised Non-rigid Groupwise Point Set Registration Based On Optimized Group Latent Descriptor
• Authors: Lingjing Wang, Xiang Li, Yi Fang
• Abstract summary: We propose a novel method named GP-Aligner to deal with the problem of non-rigid groupwise point set registration. Compared to previous non-learning approaches, our proposed method gains competitive advantages by leveraging the power of deep neural networks. GP-Aligner shows both accuracy and computational efficiency improvement in comparison with state-of-the-art methods for groupwise point set registration.
• Score: 15.900382629390297
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In this paper, we propose a novel method named GP-Aligner to deal with the problem of non-rigid groupwise point set registration. Compared to previous non-learning approaches, our proposed method gains competitive advantages by leveraging the power of deep neural networks to effectively and efficiently learn to align a large number of highly deformed 3D shapes with superior performance. Unlike most learning-based methods that use an explicit feature encoding network to extract the per-shape features and their correlations, our model leverages a model-free learnable latent descriptor to characterize the group relationship. More specifically, for a given group we first define an optimizable Group Latent Descriptor (GLD) to characterize the gruopwise relationship among a group of point sets. Each GLD is randomly initialized from a Gaussian distribution and then concatenated with the coordinates of each point of the associated point sets in the group. A neural network-based decoder is further constructed to predict the coherent drifts as the desired transformation from input groups of shapes to aligned groups of shapes. During the optimization process, GP-Aligner jointly updates all GLDs and weight parameters of the decoder network towards the minimization of an unsupervised groupwise alignment loss. After optimization, for each group our model coherently drives each point set towards a middle, common position (shape) without specifying one as the target. GP-Aligner does not require large-scale training data for network training and it can directly align groups of point sets in a one-stage optimization process. GP-Aligner shows both accuracy and computational efficiency improvement in comparison with state-of-the-art methods for groupwise point set registration. Moreover, GP-Aligner is shown great efficiency in aligning a large number of groups of real-world 3D shapes.

Related papers

• Ensemble Quadratic Assignment Network for Graph Matching [52.20001802006391]
  Graph matching is a commonly used technique in computer vision and pattern recognition. Recent data-driven approaches have improved the graph matching accuracy remarkably. We propose a graph neural network (GNN) based approach to combine the advantages of data-driven and traditional methods.
  arXiv  Detail & Related papers  (2024-03-11T06:34:05Z)
• Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification [17.284276598514502]
  We propose a novel progressive subgraph clustering algorithm based on multi-model voting and double-Gaussian based assessment. To prevent disastrous clustering results, we adopt an iterative approach that progressively increases k and employs a double-Gaussian based assessment algorithm.
  arXiv  Detail & Related papers  (2023-05-22T04:26:18Z)
• Interactive Segmentation as Gaussian Process Classification [58.44673380545409]
  Click-based interactive segmentation (IS) aims to extract the target objects under user interaction. Most of the current deep learning (DL)-based methods mainly follow the general pipelines of semantic segmentation. We propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image.
  arXiv  Detail & Related papers  (2023-02-28T14:01:01Z)
• A Variational Edge Partition Model for Supervised Graph Representation Learning [51.30365677476971]
  This paper introduces a graph generative process to model how the observed edges are generated by aggregating the node interactions over a set of overlapping node communities. We partition each edge into the summation of multiple community-specific weighted edges and use them to define community-specific GNNs. A variational inference framework is proposed to jointly learn a GNN based inference network that partitions the edges into different communities, these community-specific GNNs, and a GNN based predictor that combines community-specific GNNs for the end classification task.
  arXiv  Detail & Related papers  (2022-02-07T14:37:50Z)
• GROWL: Group Detection With Link Prediction [0.0]
  We propose a holistic approach to group detection based on Graph Neural Networks (GNNs) Our proposed method, GROup detection With Link prediction, demonstrates the effectiveness of a GNN based approach. Our results show that a GNN based approach can significantly improve accuracy across different camera views.
  arXiv  Detail & Related papers  (2021-11-08T11:52:48Z)
• Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction [19.71671771503269]
  We develop a novel framework that adds regularizers of the sparse group lasso to a family of adaptives in deep learning. We establish proven convergence guarantees in the theoretically convex settings. Our methods can achieve extremely high sparsity with significantly better or highly competitive performance.
  arXiv  Detail & Related papers  (2021-07-30T05:33:43Z)
• Learning Spatial Context with Graph Neural Network for Multi-Person Pose Grouping [71.59494156155309]
  Bottom-up approaches for image-based multi-person pose estimation consist of two stages: keypoint detection and grouping. In this work, we formulate the grouping task as a graph partitioning problem, where we learn the affinity matrix with a Graph Neural Network (GNN) The learned geometry-based affinity is further fused with appearance-based affinity to achieve robust keypoint association.
  arXiv  Detail & Related papers  (2021-04-06T09:21:14Z)
• Self-supervised Geometric Perception [96.89966337518854]
  Self-supervised geometric perception is a framework to learn a feature descriptor for correspondence matching without any ground-truth geometric model labels. We show that SGP achieves state-of-the-art performance that is on-par or superior to the supervised oracles trained using ground-truth labels.
  arXiv  Detail & Related papers  (2021-03-04T15:34:43Z)
• Determinantal consensus clustering [77.34726150561087]
  We propose the use of determinantal point processes or DPP for the random restart of clustering algorithms. DPPs favor diversity of the center points within subsets. We show through simulations that, contrary to DPP, this technique fails both to ensure diversity, and to obtain a good coverage of all data facets.
  arXiv  Detail & Related papers  (2021-02-07T23:48:24Z)
• Self-grouping Convolutional Neural Networks [30.732298624941738]
  We propose a novel method of designing self-grouping convolutional neural networks, called SG-CNN. For each filter, we first evaluate the importance value of their input channels to identify the importance vectors. Using the resulting emphdata-dependent centroids, we prune the less important connections, which implicitly minimizes the accuracy loss of the pruning.
  arXiv  Detail & Related papers  (2020-09-29T06:24:32Z)

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.