DV-Matcher: Deformation-based Non-Rigid Point Cloud Matching Guided by Pre-trained Visual Features

• URL: http://arxiv.org/abs/2408.08568v2
• Date: Sat, 01 Mar 2025 04:59:27 GMT
• Title: DV-Matcher: Deformation-based Non-Rigid Point Cloud Matching Guided by Pre-trained Visual Features
• Authors: Zhangquan Chen, Puhua Jiang, Ruqi Huang,
• Abstract summary: We present DV-Matcher, a learning-based framework for estimating dense correspondences between non-rigidly deformable point clouds.<n> Experimental results show that our method achieves state-of-the-art results in matching non-rigid point clouds in both near-isometric and heterogeneous shape collection.
• Score: 1.3030624795284795
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present DV-Matcher, a novel learning-based framework for estimating dense correspondences between non-rigidly deformable point clouds. Learning directly from unstructured point clouds without meshing or manual labelling, our framework delivers high-quality dense correspondences, which is of significant practical utility in point cloud processing. Our key contributions are two-fold: First, we propose a scheme to inject prior knowledge from pre-trained vision models into geometric feature learning, which effectively complements the local nature of geometric features with global and semantic information; Second, we propose a novel deformation-based module to promote the extrinsic alignment induced by the learned correspondences, which effectively enhances the feature learning. Experimental results show that our method achieves state-of-the-art results in matching non-rigid point clouds in both near-isometric and heterogeneous shape collection as well as more realistic partial and noisy data.

Related papers

• Self-supervised 3D Point Cloud Completion via Multi-view Adversarial Learning [61.14132533712537]
  We propose MAL-SPC, a framework that effectively leverages both object-level and category-specific geometric similarities to complete missing structures. Our MAL-SPC does not require any 3D complete supervision and only necessitates a single partial point cloud for each object.
  arXiv  Detail & Related papers  (2024-07-13T06:53:39Z)
• Point Cloud Compression with Implicit Neural Representations: A Unified Framework [54.119415852585306]
  We present a pioneering point cloud compression framework capable of handling both geometry and attribute components. Our framework utilizes two coordinate-based neural networks to implicitly represent a voxelized point cloud. Our method exhibits high universality when contrasted with existing learning-based techniques.
  arXiv  Detail & Related papers  (2024-05-19T09:19:40Z)
• Bidirectional Knowledge Reconfiguration for Lightweight Point Cloud Analysis [74.00441177577295]
  Point cloud analysis faces computational system overhead, limiting its application on mobile or edge devices. This paper explores feature distillation for lightweight point cloud models. We propose bidirectional knowledge reconfiguration to distill informative contextual knowledge from the teacher to the student.
  arXiv  Detail & Related papers  (2023-10-08T11:32:50Z)
• Variational Relational Point Completion Network for Robust 3D Classification [59.80993960827833]
  Vari point cloud completion methods tend to generate global shape skeletons hence lack fine local details. This paper proposes a variational framework, point Completion Network (VRCNet) with two appealing properties. VRCNet shows great generalizability and robustness on real-world point cloud scans.
  arXiv  Detail & Related papers  (2023-04-18T17:03:20Z)
• Self-Supervised Learning for Multimodal Non-Rigid 3D Shape Matching [15.050801537501462]
  We introduce a self-supervised multimodal learning strategy that combines mesh-based functional map regularisation with a contrastive loss that couples mesh and point cloud data. Our shape matching approach allows to obtain intramodal correspondences for triangle meshes, complete point clouds, and partially observed point clouds. We demonstrate that our method achieves state-of-the-art results on several challenging benchmark datasets.
  arXiv  Detail & Related papers  (2023-03-20T09:47:02Z)
• Self-Supervised Feature Learning from Partial Point Clouds via Pose Disentanglement [35.404285596482175]
  We propose a novel self-supervised framework to learn informative representations from partial point clouds. We leverage partial point clouds scanned by LiDAR that contain both content and pose attributes. Our method not only outperforms existing self-supervised methods, but also shows a better generalizability across synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2022-01-09T14:12:50Z)
• Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion [38.05362492645094]
  Real point cloud scenes can intuitively capture complex surroundings in the real world, but due to 3D data's raw nature, it is very challenging for machine perception. We concentrate on the essential visual task, semantic segmentation, for large-scale point cloud data collected in reality. By comparing with state-of-the-art networks on three different benchmarks, we demonstrate the effectiveness of our network.
  arXiv  Detail & Related papers  (2021-03-12T04:13:20Z)
• 3D Object Classification on Partial Point Clouds: A Practical Perspective [91.81377258830703]
  A point cloud is a popular shape representation adopted in 3D object classification. This paper introduces a practical setting to classify partial point clouds of object instances under any poses. A novel algorithm in an alignment-classification manner is proposed in this paper.
  arXiv  Detail & Related papers  (2020-12-18T04:00:56Z)
• SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization [52.20602782690776]
  It is expensive and tedious to obtain large scale paired sparse-canned point sets for training from real scanned sparse data. We propose a self-supervised point cloud upsampling network, named SPU-Net, to capture the inherent upsampling patterns of points lying on the underlying object surface. We conduct various experiments on both synthetic and real-scanned datasets, and the results demonstrate that we achieve comparable performance to the state-of-the-art supervised methods.
  arXiv  Detail & Related papers  (2020-12-08T14:14:09Z)
• End-to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences [17.70819292121181]
  3D Point cloud registration is still a very challenging topic due to the difficulty in finding the rigid transformation between two point clouds. In this paper, we present an end-to-end deep-learning based approach to resolve the point cloud registration problem.
  arXiv  Detail & Related papers  (2020-11-30T06:55:05Z)
• Deep Magnification-Flexible Upsampling over 3D Point Clouds [103.09504572409449]
  We propose a novel end-to-end learning-based framework to generate dense point clouds. We first formulate the problem explicitly, which boils down to determining the weights and high-order approximation errors. Then, we design a lightweight neural network to adaptively learn unified and sorted weights as well as the high-order refinements.
  arXiv  Detail & Related papers  (2020-11-25T14:00:18Z)
• PointManifold: Using Manifold Learning for Point Cloud Classification [5.705680763604835]
  We propose a point cloud classification method based on graph neural network and manifold learning. This paper uses manifold learning algorithms to embed point cloud features for better considering continuity on the surface. Experiments show that the proposed point cloud classification methods obtain the mean class accuracy (mA) of 90.2% and the overall accuracy (oA) of 93.2%.
  arXiv  Detail & Related papers  (2020-10-14T16:28:19Z)
• Mapping in a cycle: Sinkhorn regularized unsupervised learning for point cloud shapes [47.49826669394906]
  We propose an unsupervised learning framework for finding dense correspondences between point cloud shapes. In order to learn discriminative pointwise features from point cloud data, we incorporate in the formulation a regularization term based on Sinkhorn normalization. We show that the learned pointwise features can be leveraged by supervised methods to improve the part segmentation performance.
  arXiv  Detail & Related papers  (2020-07-19T05:21:33Z)
• Shape-Oriented Convolution Neural Network for Point Cloud Analysis [59.405388577930616]
  Point cloud is a principal data structure adopted for 3D geometric information encoding. Shape-oriented message passing scheme dubbed ShapeConv is proposed to focus on the representation learning of the underlying shape formed by each local neighboring point.
  arXiv  Detail & Related papers  (2020-04-20T16:11:51Z)
• Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation [38.61801196027949]
  We present a learning-based method for interpolating and manipulating 3D shapes represented as point clouds. Our approach is based on constructing a dual encoding space that enables synthesis shape and, at the same time, provides links to the intrinsic shape information.
  arXiv  Detail & Related papers  (2020-04-03T16:28:55Z)

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.