Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning

• URL: http://arxiv.org/abs/2107.01886v1
• Date: Mon, 5 Jul 2021 09:17:45 GMT
• Title: Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning
• Authors: Bi'an Du, Xiang Gao, Wei Hu, Xin Li
• Abstract summary: We propose a novel self-contrastive learning for self-supervised point cloud representation learning. We exploit self-similar point cloud patches within a single point cloud as positive samples and otherwise negative ones to facilitate the task of contrastive learning. Experimental results show that the proposed method achieves state-of-the-art performance on widely used benchmark datasets.
• Score: 17.55440737986014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point clouds have attracted increasing attention as a natural representation of 3D shapes. Significant progress has been made in developing methods for point cloud analysis, which often requires costly human annotation as supervision in practice. To address this issue, we propose a novel self-contrastive learning for self-supervised point cloud representation learning, aiming to capture both local geometric patterns and nonlocal semantic primitives based on the nonlocal self-similarity of point clouds. The contributions are two-fold: on the one hand, instead of contrasting among different point clouds as commonly employed in contrastive learning, we exploit self-similar point cloud patches within a single point cloud as positive samples and otherwise negative ones to facilitate the task of contrastive learning. Such self-contrastive learning is well aligned with the emerging paradigm of self-supervised learning for point cloud analysis. On the other hand, we actively learn hard negative samples that are close to positive samples in the representation space for discriminative feature learning, which are sampled conditional on each anchor patch leveraging on the degree of self-similarity. Experimental results show that the proposed method achieves state-of-the-art performance on widely used benchmark datasets for self-supervised point cloud segmentation and transfer learning for classification.

Related papers

• PointMoment:Mixed-Moment-based Self-Supervised Representation Learning for 3D Point Clouds [11.980787751027872]
  We propose PointMoment, a novel framework for point cloud self-supervised representation learning. Our framework does not require any special techniques such as asymmetric network architectures, gradient stopping, etc.
  arXiv  Detail & Related papers  (2023-12-06T08:49:55Z)
• Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos [71.20376514273367]
  We propose a unified point cloud video self-supervised learning framework for object-centric and scene-centric data. Our method outperforms supervised counterparts on a wide range of downstream tasks.
  arXiv  Detail & Related papers  (2023-08-18T02:17:47Z)
• Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning [26.773995001469505]
  We design point cloud sequence based Contrastive Prediction and Reconstruction (CPR), to collaboratively learn more comprehensive representations. We conduct experiments on four point cloud sequence benchmarks, and report the results under multiple experimental settings.
  arXiv  Detail & Related papers  (2023-05-22T12:09:51Z)
• D-Net: Learning for Distinctive Point Clouds by Self-Attentive Point Searching and Learnable Feature Fusion [48.57170130169045]
  We propose D-Net to learn for distinctive point clouds based on a self-attentive point searching and a learnable feature fusion. To generate a compact feature representation for each distinctive point set, a stacked self-gated convolution is proposed to extract the distinctive features. The results show that the learned distinction distribution of a point cloud is highly consistent with objects of the same class and different from objects of other classes.
  arXiv  Detail & Related papers  (2023-05-10T02:19:00Z)
• Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation [79.60988242843437]
  We propose a novel approach that achieves self-supervised and magnification-flexible point clouds upsampling simultaneously. Experimental results demonstrate that our self-supervised learning based scheme achieves competitive or even better performance than supervised learning based state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-18T07:18:25Z)
• Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey [104.71816962689296]
  Unsupervised point cloud representation learning has attracted increasing attention due to the constraint in large-scale point cloud labelling. This paper provides a comprehensive review of unsupervised point cloud representation learning using deep neural networks.
  arXiv  Detail & Related papers  (2022-02-28T07:46:05Z)
• 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)
• NeeDrop: Self-supervised Shape Representation from Sparse Point Clouds using Needle Dropping [80.2485282512607]
  We introduce NeeDrop, a self-supervised method for learning shape representations from sparse point clouds. No shape knowledge is required and the point cloud can be highly sparse, e.g., as lidar point clouds acquired by vehicles. We obtain quantitative results on par with existing supervised approaches on shape reconstruction datasets.
  arXiv  Detail & Related papers  (2021-11-30T08:35:20Z)
• Unsupervised Representation Learning for 3D Point Cloud Data [66.92077180228634]
  We propose a simple yet effective approach for unsupervised point cloud learning. In particular, we identify a very useful transformation which generates a good contrastive version of an original point cloud. We conduct experiments on three downstream tasks which are 3D object classification, shape part segmentation and scene segmentation.
  arXiv  Detail & Related papers  (2021-10-13T10:52:45Z)

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.