Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning

• URL: http://arxiv.org/abs/2202.04241v1
• Date: Wed, 9 Feb 2022 02:51:59 GMT
• Title: Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning
• Authors: Kexue Fu and Peng Gao and Renrui Zhang and Hongsheng Li and Yu Qiao and Manning Wang
• Abstract summary: We propose a simple and general framework for self-supervised point cloud representation learning. Inspired by how human beings understand the world, we utilize knowledge distillation to learn both global shape information and the relationship between global shape and local structures. Our method achieves the state-of-the-art performance on linear classification and multiple other downstream tasks.
• Score: 53.90317574898643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a simple and general framework for self-supervised point cloud representation learning. Human beings understand the 3D world by extracting two levels of information and establishing the relationship between them. One is the global shape of an object, and the other is the local structures of it. However, few existing studies in point cloud representation learning explored how to learn both global shapes and local-to-global relationships without a specified network architecture. Inspired by how human beings understand the world, we utilize knowledge distillation to learn both global shape information and the relationship between global shape and local structures. At the same time, we combine contrastive learning with knowledge distillation to make the teacher network be better updated. Our method achieves the state-of-the-art performance on linear classification and multiple other downstream tasks. Especially, we develop a variant of ViT for 3D point cloud feature extraction, which also achieves comparable results with existing backbones when combined with our framework, and visualization of the attention maps show that our model does understand the point cloud by combining the global shape information and multiple local structural information, which is consistent with the inspiration of our representation learning method. Our code will be released soon.

Related papers

• HVDistill: Transferring Knowledge from Images to Point Clouds via Unsupervised Hybrid-View Distillation [106.09886920774002]
  We present a hybrid-view-based knowledge distillation framework, termed HVDistill, to guide the feature learning of a point cloud neural network. Our method achieves consistent improvements over the baseline trained from scratch and significantly out- performs the existing schemes.
  arXiv  Detail & Related papers  (2024-03-18T14:18:08Z)
• Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration [17.420425069785946]
  We present a novel Cross-Modal Information-Guided Network (CMIGNet) for point cloud registration. We first incorporate the projected images from the point clouds and fuse the cross-modal features using the attention mechanism. We employ two contrastive learning strategies, namely overlapping contrastive learning and cross-modal contrastive learning.
  arXiv  Detail & Related papers  (2023-11-02T12:56:47Z)
• 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)
• Multi-network Contrastive Learning Based on Global and Local Representations [4.190134425277768]
  This paper proposes a multi-network contrastive learning framework based on global and local representations. We introduce global and local feature information for self-supervised contrastive learning through multiple networks. The framework also expands the number of samples used for contrast and improves the training efficiency of the model.
  arXiv  Detail & Related papers  (2023-06-28T05:30:57Z)
• 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)
• Point Discriminative Learning for Unsupervised Representation Learning on 3D Point Clouds [54.31515001741987]
  We propose a point discriminative learning method for unsupervised representation learning on 3D point clouds. We achieve this by imposing a novel point discrimination loss on the middle level and global level point features. Our method learns powerful representations and achieves new state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-08-04T15:11:48Z)
• Cascaded Refinement Network for Point Cloud Completion [74.80746431691938]
  We propose a cascaded refinement network together with a coarse-to-fine strategy to synthesize the detailed object shapes. Considering the local details of partial input with the global shape information together, we can preserve the existing details in the incomplete point set. We also design a patch discriminator that guarantees every local area has the same pattern with the ground truth to learn the complicated point distribution.
  arXiv  Detail & Related papers  (2020-04-07T13:03:29Z)
• Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds [109.0016923028653]
  We learn point cloud representation by bidirectional reasoning between the local structures and the global shape without human supervision. We show that our unsupervised model surpasses the state-of-the-art supervised methods on both synthetic and real-world 3D object classification datasets.
  arXiv  Detail & Related papers  (2020-03-29T08:26:08Z)

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.