论文标题
用于点云分类和细分的密集分辨率网络
Dense-Resolution Network for Point Cloud Classification and Segmentation
论文作者
论文摘要
点云分析引起了人工智能研究的关注，因为它可以广泛用于机器人技术，增强现实，自动驾驶的应用中。但是，由于不规则，无序和稀疏性，这总是具有挑战性的。在本文中，我们提出了一个名为“密集分辨率网络”（DRNET）的新型网络，以进行点云分析。我们的DRNET旨在从不同分辨率中的点云中学习本地点功能。为了更有效地学习本地点组，我们提出了一种新颖的分组方法，用于本地邻居搜索和一个错误最小化模块，用于捕获本地特征。除了在广泛使用的点云分割和分类基准上验证网络外，我们还测试和可视化组件的性能。与其他最先进的方法相比，我们的网络在ModelNet40，Shapenet合成和ScanObjectnn真实点云数据集上显示出优势。
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.
