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 language | English (US) |
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Pages (from-to) | 9163-9174 |
Number of pages | 12 |
Journal | IEEE Transactions on Multimedia |
Volume | 26 |
DOIs | |
State | Published - 2024 |
Externally published | Yes |
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