Point-Voxel Based Geometry-Adaptive Network for 3D Point Cloud Analysis

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外文摘要：Point cloud analysis is challenging because of the unordered and irregular data structure of point clouds.To describe geometric information in point clouds,existing methods mainly use convolution,graph,and attention operations to construct sophisticated local aggregation operators.These operators work well in extracting local information but bring unfavorable inference latency due to high computation complexity.To solve the above problem,this paper presents a nov-el point-voxel based geometry-adaptive network(PVGANet),which combines multiple representations of point and voxel to describe the point cloud from different granularities and can obtain features of different scales effectively.To extract fine-grained geometric features,we design the position-adaptive pooling operator,which uses point pairs'relative position and feature similarity to weight and aggregate the point features at local areas of point clouds.To extract coarse-grained local features,we design a depth-wise convolution operator,which conducts the depth-wise convolution on voxel grids.With an easy addition,fine-grained geometric and coarse-grained local features can be fused,and we can use the geometry-adaptive fused features to complete the efficient shape analysis of point clouds,such as shape classification and part seg-mentation.Extensive experiments on ModelNet40,ScanObjectNN,and ShapeNet Part benchmarks demonstrate that our PVGANet achieves competitive performance compared with the related methods.

外文关键词：

point cloudgeometric featuremulti-representationpart segmentationshape classification

作者：

赵天孟、曾慧、张保庆、刘红敏、樊彬

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作者单位：

Beijing Engineering Research Center of Industrial Spectrum Imaging,School of Automation and Electrial Engineering University of Science and Technology Beijing,Beijing 100083,China

Shunde Innovation School,University of Science and Technology Beijing,Foshan 528399,China

Beijing Institute of Electronic System Engineering,Beijing 100854,China

School of Intelligence Science and Technology,University of Science and Technology Beijing,Beijing 100083,China

Institute of Artificial Intelligence,University of Science and Technology Beijing,Beijing 100083,China

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出版年：

2024

DOI：

10.1007/s11390-024-3521-x

计算机科学技术学报(英文版)

中国计算机学会

计算机科学技术学报(英文版)

CSTPCD

影响因子：0.432

ISSN：1000-9000

年,卷(期)：2024.39(5)