PointManifoldCut: Point-wise Augmentation in the Manifold for Point Clouds

• URL: http://arxiv.org/abs/2109.07324v1
• Date: Wed, 15 Sep 2021 14:31:42 GMT
• Title: PointManifoldCut: Point-wise Augmentation in the Manifold for Point Clouds
• Authors: Tianfang Zhu, Yue Guan, Anan Li
• Abstract summary: This paper proposes a mix-up augmentation approach, PointManifoldCut, which replaces the neural network embedded points. Experiments show that our proposed approach provides a competitive performance on point cloud classification and segmentation.
• Score: 2.756263525080896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Augmentation can benefit point cloud learning due to the limited availability of large-scale public datasets. This paper proposes a mix-up augmentation approach, PointManifoldCut, which replaces the neural network embedded points, rather than the Euclidean space coordinates. This approach takes the advantage that points at the higher levels of the neural network are already trained to embed its neighbors relations and mixing these representation will not mingle the relation between itself and its label. This allows to regularize the parameter space as the other augmentation methods but without worrying about the proper label of the replaced points. The experiments show that our proposed approach provides a competitive performance on point cloud classification and segmentation when it is combined with the cutting-edge vanilla point cloud networks. The result shows a consistent performance boosting compared to other state-of-the-art point cloud augmentation method, such as PointMixup and PointCutMix. The code of this paper is available at: https://github.com/fun0515/PointManifoldCut.

Related papers

• P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising [81.92854168911704]
  We tackle the task of point cloud denoising through a novel framework that adapts Diffusion Schr"odinger bridges to points clouds. Experiments on object datasets show that P2P-Bridge achieves significant improvements over existing methods.
  arXiv  Detail & Related papers  (2024-08-29T08:00:07Z)
• Point2Point : A Framework for Efficient Deep Learning on Hilbert sorted Point Clouds with applications in Spatio-Temporal Occupancy Prediction [0.0]
  We propose a novel approach to representing point clouds as a locality preserving 1D ordering induced by the Hilbert space-filling curve. We also introduce Point2Point, a neural architecture that can effectively learn on Hilbert-sorted point clouds.
  arXiv  Detail & Related papers  (2023-06-28T15:30:08Z)
• FreePoint: Unsupervised Point Cloud Instance Segmentation [72.64540130803687]
  We propose FreePoint, for underexplored unsupervised class-agnostic instance segmentation on point clouds. We represent point features by combining coordinates, colors, and self-supervised deep features. Based on the point features, we segment point clouds into coarse instance masks as pseudo labels, which are used to train a point cloud instance segmentation model.
  arXiv  Detail & Related papers  (2023-05-11T16:56:26Z)
• PointPatchMix: Point Cloud Mixing with Patch Scoring [58.58535918705736]
  We propose PointPatchMix, which mixes point clouds at the patch level and generates content-based targets for mixed point clouds. Our approach preserves local features at the patch level, while the patch scoring module assigns targets based on the content-based significance score from a pre-trained teacher model. With Point-MAE as our baseline, our model surpasses previous methods by a significant margin, achieving 86.3% accuracy on ScanObjectNN and 94.1% accuracy on ModelNet40.
  arXiv  Detail & Related papers  (2023-03-12T14:49:42Z)
• Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space [25.57569871876213]
  We propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space (content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class. We also introduce an Inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately.
  arXiv  Detail & Related papers  (2023-03-08T14:11:05Z)
• Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud [69.36717778451667]
  Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. We propose an effective weakly supervised method containing two components to solve the problem. The experimental results show the large gain against existing weakly supervised and comparable results to fully supervised methods.
  arXiv  Detail & Related papers  (2022-12-09T09:42:26Z)
• Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding [61.30276576646909]
  We propose an augmentation-free unsupervised approach for point clouds to learn transferable point-level features via soft clustering, named SoftClu. We exploit the affiliation of points to their clusters as a proxy to enable self-training through a pseudo-label prediction task.
  arXiv  Detail & Related papers  (2022-10-06T10:18:16Z)
• Attribute Artifacts Removal for Geometry-based Point Cloud Compression [43.60640890971367]
  Geometry-based point cloud compression (G-PCC) can achieve remarkable compression efficiency for point clouds. It still leads to serious attribute compression artifacts, especially under low scenarios. We propose a Multi-Scale Graph Attention Network (MSGAT) to remove the artifacts of point cloud attributes.
  arXiv  Detail & Related papers  (2021-12-01T15:21:06Z)
• PointCutMix: Regularization Strategy for Point Cloud Classification [7.6904253666422395]
  We propose a simple and effective augmentation method for the point cloud data, named PointCutMix. It finds the optimal assignment between two point clouds and generates new training data by replacing the points in one sample with their optimal assigned pairs.
  arXiv  Detail & Related papers  (2021-01-05T11:39:06Z)
• PointMixup: Augmentation for Point Clouds [65.61212404598524]
  We introduce PointMixup, a method that generates new examples through an optimal assignment of the path function between two point clouds. We show the potential of PointMixup for point cloud classification, especially when examples are scarce.
  arXiv  Detail & Related papers  (2020-08-14T13:57:20Z)

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.