Details

Autor(en) / Beteiligte

• Bin Shen
• Li Li
• Xinrong Hu
• Shengyi Guo
• Jin Huang
• Zhiyao Liang

Titel

A Point Cloud Upsampling Adversarial Network Based on Residual Multi-Scale Off-Set Attention

Ist Teil von

• Virtual Reality & Intelligent Hardware, 2023-02, Vol.5 (1), p.81-91

Ort / Verlag

KeAi Communications Co., Ltd

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Due to the limitation of the working principle of 3D scanning equipment, the point cloud obtained by 3D scanning is usually sparse and unevenly distributed. In this paper, we propose a new Generative Adversarial Network(GAN) for point cloud upsampling, which is extended from PU-GAN. Its core architecture is to replace the traditional Self-Attention (SA) module with the implicit Laplacian Off-Set Attention(OA) module, and adjacency features are aggregated using the Multi-Scale Off-Set Attention(MSOA) module, which adaptively adjusts the receptive field to learn various structural features. Finally, Residual links were added to form our Residual Multi-Scale Off-Set Attention (RMSOA) module, which utilized multi-scale structural relationships to generate finer details. A large number of experiments show that the performance of our method is superior to the existing methods, and our model has high robustness.