2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-4.3
Paper Title HIGCNN: HIERARCHICAL INTERLEAVED GROUP CONVOLUTIONAL NEURAL NETWORKS FOR POINT CLOUDS ANALYSIS
Authors Jisheng Dang, Jun Yang, Lanzhou Jiaotong University, China
SessionMLSP-4: Machine Learning for Classification Applications 1
LocationGather.Town
Session Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-PRCL] Pattern recognition and classification
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Although previous works for point clouds analysis have achieved remarkable performance, it is difficult for them to achieve a good trade-off between accuracy and complexity. In this paper, we present an efficient and lightweight neural network for point clouds analysis, named HIGCNN, which can achieve better performance but lower complexity compared to existing methods. The key component in our approach is the hierarchical interleaved group convolution (HIGConv) module. We first present a neighborhood attention convolution (NAC) operation to fully mine fine-grained local geometric features inside each local area. With the proposed NAC, we further design a HIGConv to encode both discriminative fine-grained local geometric features and nonlocal point-wise features with fewer parameters and lower computational costs. To further capture fine-grained contextual features, we propose a multi-scale relation (MSR) module to fully explore the relationship among different scale areas. Extensive experiments show that our HIGCNN surpasses state-of-the-art approaches for classification and semantic segmentation on four benchmarks ModelNet40, S3DIS, vKITTI and SemanticKITTI in terms of accuracy and complexity.