Dense-Resolution Network for Point Cloud Classification and Segmentation

• URL: http://arxiv.org/abs/2005.06734v2
• Date: Tue, 17 Nov 2020 08:02:25 GMT
• Title: Dense-Resolution Network for Point Cloud Classification and Segmentation
• Authors: Shi Qiu, Saeed Anwar, Nick Barnes
• Abstract summary: DRNet is designed to learn local point features from the point cloud in different resolutions. In addition to validating the network on widely used point cloud segmentation and classification benchmarks, we also test and visualize the performance of the components.
• Score: 42.316932316581635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point cloud analysis is attracting attention from Artificial Intelligence research since it can be widely used in applications such as robotics, Augmented Reality, self-driving. However, it is always challenging due to irregularities, unorderedness, and sparsity. In this article, we propose a novel network named Dense-Resolution Network (DRNet) for point cloud analysis. Our DRNet is designed to learn local point features from the point cloud in different resolutions. In order to learn local point groups more effectively, we present a novel grouping method for local neighborhood searching and an error-minimizing module for capturing local features. In addition to validating the network on widely used point cloud segmentation and classification benchmarks, we also test and visualize the performance of the components. Comparing with other state-of-the-art methods, our network shows superiority on ModelNet40, ShapeNet synthetic and ScanObjectNN real point cloud datasets.

Related papers

• Collect-and-Distribute Transformer for 3D Point Cloud Analysis [82.03517861433849]
  We propose a new transformer network equipped with a collect-and-distribute mechanism to communicate short- and long-range contexts of point clouds. Results show the effectiveness of the proposed CDFormer, delivering several new state-of-the-art performances on point cloud classification and segmentation tasks.
  arXiv  Detail & Related papers  (2023-06-02T03:48:45Z)
• Dynamic Clustering Transformer Network for Point Cloud Segmentation [23.149220817575195]
  We propose a novel 3D point cloud representation network, called Dynamic Clustering Transformer Network (DCTNet) It has an encoder-decoder architecture, allowing for both local and global feature learning. Our method was evaluated on an object-based dataset (ShapeNet), an urban navigation dataset (Toronto-3D), and a multispectral LiDAR dataset.
  arXiv  Detail & Related papers  (2023-05-30T01:11:05Z)
• 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)
• ABD-Net: Attention Based Decomposition Network for 3D Point Cloud Decomposition [1.3999481573773074]
  We propose Attention Based Decomposition Network (ABD-Net) for point cloud decomposition. We show improved performance of 3D object classification using attention features based on primitive shapes in point clouds.
  arXiv  Detail & Related papers  (2021-07-09T08:39:30Z)
• 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)
• SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition [50.9889997200743]
  We tackle the problem of place recognition from point cloud data with a self-attention and orientation encoding network (SOE-Net) SOE-Net fully explores the relationship between points and incorporates long-range context into point-wise local descriptors. Experiments on various benchmark datasets demonstrate superior performance of the proposed network over the current state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-11-24T22:28:25Z)
• Multi-scale Receptive Fields Graph Attention Network for Point Cloud Classification [35.88116404702807]
  The proposed MRFGAT architecture is tested on ModelNet10 and ModelNet40 datasets. Results show it achieves state-of-the-art performance in shape classification tasks.
  arXiv  Detail & Related papers  (2020-09-28T13:01:28Z)
• Self-Sampling for Neural Point Cloud Consolidation [83.31236364265403]
  We introduce a novel technique for neural point cloud consolidation which learns from only the input point cloud. We repeatedly self-sample the input point cloud with global subsets that are used to train a deep neural network. We demonstrate the ability to consolidate point sets from a variety of shapes, while eliminating outliers and noise.
  arXiv  Detail & Related papers  (2020-08-14T17:16:02Z)
• TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations [20.318695890515613]
  We propose an autoencoder, TearingNet, which tackles the challenging task of representing point clouds using a fixed-length descriptor. Our TearingNet is characterized by a proposed Tearing network module and a Folding network module interacting with each other iteratively. Experimentation shows the superiority of our proposal in terms of reconstructing point clouds as well as generating more topology-friendly representations than benchmarks.
  arXiv  Detail & Related papers  (2020-06-17T22:42:43Z)
• GRNet: Gridding Residual Network for Dense Point Cloud Completion [54.43648460932248]
  Estimating the complete 3D point cloud from an incomplete one is a key problem in many vision and robotics applications. We propose a novel Gridding Residual Network (GRNet) for point cloud completion. Experimental results indicate that the proposed GRNet performs favorably against state-of-the-art methods on the ShapeNet, Completion3D, and KITTI benchmarks.
  arXiv  Detail & Related papers  (2020-06-06T02:46:39Z)

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.