PointCutMix: Regularization Strategy for Point Cloud Classification

• URL: http://arxiv.org/abs/2101.01461v2
• Date: Fri, 5 Feb 2021 08:03:01 GMT
• Title: PointCutMix: Regularization Strategy for Point Cloud Classification
• Authors: Jinlai Zhang, Lyujie Chen, Bo Ouyang, Binbin Liu, Jihong Zhu, Yujing Chen, Yanmei Meng, Danfeng Wu
• Abstract summary: 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.
• Score: 7.6904253666422395
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As 3D point cloud analysis has received increasing attention, the insufficient scale of point cloud datasets and the weak generalization ability of networks become prominent. In this paper, we propose a simple and effective augmentation method for the point cloud data, named PointCutMix, to alleviate those problems. 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. Two replacement strategies are proposed to adapt to the accuracy or robustness requirement for different tasks, one of which is to randomly select all replacing points while the other one is to select k nearest neighbors of a single random point. Both strategies consistently and significantly improve the performance of various models on point cloud classification problems. By introducing the saliency maps to guide the selection of replacing points, the performance further improves. Moreover, PointCutMix is validated to enhance the model robustness against the point attack. It is worth noting that when using as a defense method, our method outperforms the state-of-the-art defense algorithms. The code is available at:https://github.com/cuge1995/PointCutMix

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)
• Point Cloud Pre-training with Diffusion Models [62.12279263217138]
  We propose a novel pre-training method called Point cloud Diffusion pre-training (PointDif) PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification, segmentation and detection.
  arXiv  Detail & Related papers  (2023-11-25T08:10:05Z)
• 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)
• HybridPoint: Point Cloud Registration Based on Hybrid Point Sampling and Matching [8.255850058549653]
  We propose a HybridPoint-based network to find more robust and accurate correspondences. Our model achieves state-of-the-art performance on 3DMatch, 3DLoMatch, and KITTI odometry.
  arXiv  Detail & Related papers  (2023-03-29T08:28:45Z)
• 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)
• Shape-invariant 3D Adversarial Point Clouds [111.72163188681807]
  Adversary and invisibility are two fundamental but conflict characters of adversarial perturbations. Previous adversarial attacks on 3D point cloud recognition have often been criticized for their noticeable point outliers. We propose a novel Point-Cloud Sensitivity Map to boost both the efficiency and imperceptibility of point perturbations.
  arXiv  Detail & Related papers  (2022-03-08T12:21:35Z)
• DeepBBS: Deep Best Buddies for Point Cloud Registration [55.12101890792121]
  DeepBBS is a novel method for learning a representation that takes into account the best buddy distance between points during training. Our experiments show improved performance compared to previous methods.
  arXiv  Detail & Related papers  (2021-10-06T19:00:07Z)
• PointManifoldCut: Point-wise Augmentation in the Manifold for Point Clouds [2.756263525080896]
  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.
  arXiv  Detail & Related papers  (2021-09-15T14:31:42Z)

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.