DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation

• URL: http://arxiv.org/abs/2108.04023v1
• Date: Mon, 9 Aug 2021 13:23:54 GMT
• Title: DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation
• Authors: Maosheng Ye, Shuangjie Xu, Tongyi Cao, Qifeng Chen
• Abstract summary: DRINet serves as the basic network structure for dual-representation learning. Our network achieves state-of-the-art results for point cloud classification and segmentation tasks. For large-scale outdoor scenarios, our method outperforms state-of-the-art methods with a real-time inference speed of 62ms per frame.
• Score: 45.768040873409824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel and flexible architecture for point cloud segmentation with dual-representation iterative learning. In point cloud processing, different representations have their own pros and cons. Thus, finding suitable ways to represent point cloud data structure while keeping its own internal physical property such as permutation and scale-invariant is a fundamental problem. Therefore, we propose our work, DRINet, which serves as the basic network structure for dual-representation learning with great flexibility at feature transferring and less computation cost, especially for large-scale point clouds. DRINet mainly consists of two modules called Sparse Point-Voxel Feature Extraction and Sparse Voxel-Point Feature Extraction. By utilizing these two modules iteratively, features can be propagated between two different representations. We further propose a novel multi-scale pooling layer for pointwise locality learning to improve context information propagation. Our network achieves state-of-the-art results for point cloud classification and segmentation tasks on several datasets while maintaining high runtime efficiency. For large-scale outdoor scenarios, our method outperforms state-of-the-art methods with a real-time inference speed of 62ms per frame.

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 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)
• Clustering based Point Cloud Representation Learning for 3D Analysis [80.88995099442374]
  We propose a clustering based supervised learning scheme for point cloud analysis. Unlike current de-facto, scene-wise training paradigm, our algorithm conducts within-class clustering on the point embedding space. Our algorithm shows notable improvements on famous point cloud segmentation datasets.
  arXiv  Detail & Related papers  (2023-07-27T03:42:12Z)
• Point2Vec for Self-Supervised Representation Learning on Point Clouds [66.53955515020053]
  We extend data2vec to the point cloud domain and report encouraging results on several downstream tasks. We propose point2vec, which unleashes the full potential of data2vec-like pre-training on point clouds.
  arXiv  Detail & Related papers  (2023-03-29T10:08:29Z)
• Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network [22.9434434107516]
  We develop a new multi-layer transformer network for few-shot point cloud semantic segmentation. Our method achieves the new state-of-the-art performance, with 15% less inference time, over existing few-shot 3D point cloud segmentation models.
  arXiv  Detail & Related papers  (2023-03-28T00:27:54Z)
• 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)
• DRINet++: Efficient Voxel-as-point Point Cloud Segmentation [41.89569852619741]
  DRINet++ extends DRINet by enhancing the sparsity and geometric properties of a point cloud with a voxel-as-point principle. Our state-of-the-art outdoor point cloud segmentation achieves convergence and alleviates the memory consumption problem.
  arXiv  Detail & Related papers  (2021-11-16T09:22:15Z)
• FatNet: A Feature-attentive Network for 3D Point Cloud Processing [1.502579291513768]
  We introduce a novel feature-attentive neural network layer, a FAT layer, that combines both global point-based features and local edge-based features in order to generate better embeddings. Our architecture achieves state-of-the-art results on the task of point cloud classification, as demonstrated on the ModelNet40 dataset.
  arXiv  Detail & Related papers  (2021-04-07T23:13:56Z)

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.