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MinkLoc3D:基于点云的大规模地点识别(CS CV)

2020-12-07 19:23:58 凌茜

本文提出了一种基于学习的方法来计算判别三维点云描述子,用于位置识别。现有的方法,比如PointNetVLAD,是基于无序点云表示的。他们使用PointNet作为提取局部特征的第一个处理步骤,这些局部特征随后被聚合成一个全局描述符。PointNet体系结构不太适合捕获本地几何结构。因此,最先进的方法通过添加不同的机制来捕获本地上下文信息,比如图卷积网络或使用手工制作的特性,来增强普通的PointNet体系结构。我们提出了一种替代方法,称为MinkLoc3D,以计算一个判别三维点云描述符,基于稀疏体素化点云表示和稀疏三维卷积。该方法具有简单、高效的结构。对标准基准测试的评估证明,MinkLoc3D的性能优于当前的最先进水平。

原文题目:MinkLoc3D: Point Cloud Based Large-Scale Place Recognition

原文:The paper presents a learning-based method for computing a discriminative 3D point cloud descriptor for place recognition purposes. Existing methods, such as PointNetVLAD, are based on unordered point cloud representation. They use PointNet as the first processing step to extract local features, which are later aggregated into a global descriptor. The PointNet architecture is not well suited to capture local geometric structures. Thus, state-of-the-art methods enhance vanilla PointNet architecture by adding different mechanism to capture local contextual information, such as graph convolutional networks or using hand-crafted features. We present an alternative approach, dubbed MinkLoc3D, to compute a discriminative 3D point cloud descriptor, based on a sparse voxelized point cloud representation and sparse 3D convolutions. The proposed method has a simple and efficient architecture. Evaluation on standard benchmarks proves that MinkLoc3D outperforms current state-of-the-art. Our code is publicly available on the project website: this https URL

原文作者:Jacek Komorowski

原文地址:https://arxiv.org/abs/2011.04530

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https://cloud.tencent.com/developer/article/1748731