Dynamic View Aggregation for Multi-View 3D Shape Recognition

Yuan Zhou, Zhongqi Sun, Shuwei Huo, Sun Yuan Kung

Research output: Contribution to journalArticlepeer-review

Abstract

In the field of 3D shape recognition, the view-based approach has achieved state-of-the-art performance. A major challenge that needs to be addressed by the view-based approach is how to effectively aggregate multi-view features to obtain a better 3D shape representation. Existing methods which rely on networks with static parameters for feature aggregation adversely coerce the network to learn a general feature aggregation strategy for all inputs, ignoring the diversity of input 3D shapes in real-world scenarios. In this work, we propose a novel Dynamic View Aggregation Network called DVA-Net to address this challenge. DVA-Net can dynamically adjust the network parameter depending on the input 3D shapes to flexibly fuse multi-view information. The shape-specific parameter adaptation is achieved by our designed Dynamic Relation-aware Aggregation module, dubbed DRA module. It is responsible for learning relations among views and adaptively integrating multi-view features. Comprehensive experiments on benchmark datasets demonstrate that our proposed method achieves state-of-the-art performance for 3D shape classification and retrieval.

Original languageEnglish (US)
Pages (from-to)9163-9174
Number of pages12
JournalIEEE Transactions on Multimedia
Volume26
DOIs
StatePublished - 2024
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • 3D shape recognition
  • dynamic view aggregation
  • view-based method

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