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ESP-PCT: Enhanced VR Semantic Performance through Efficient Compression of Temporal and Spatial Redundancies in Point Cloud Transformers

Luoyu Mei, Shuai Wang, Yun Cheng, Ruofeng Liu, Zhimeng Yin, Wenchao Jiang, Shuai Wang, Wei Gong

Abstract

Semantic recognition is pivotal in virtual reality (VR) applications, enabling immersive and interactive experiences. A promising approach is utilizing millimeter-wave (mmWave) signals to generate point clouds. However, the high computational and memory demands of current mmWave point cloud models hinder their efficiency and reliability. To address this limitation, our paper introduces ESP-PCT, a novel Enhanced Semantic Performance Point Cloud Transformer with a two-stage semantic recognition framework tailored for VR applications. ESP-PCT takes advantage of the accuracy of sensory point cloud data and optimizes the semantic recognition process, where the localization and focus stages are trained jointly in an end-to-end manner. We evaluate ESP-PCT on various VR semantic recognition conditions, demonstrating substantial enhancements in recognition efficiency. Notably, ESP-PCT achieves a remarkable accuracy of 93.2% while reducing the computational requirements (FLOPs) by 76.9% and memory usage by 78.2% compared to the existing Point Transformer model simultaneously. These underscore ESP-PCT's potential in VR semantic recognition by achieving high accuracy and reducing redundancy. The code and data of this project are available at \url{https://github.com/lymei-SEU/ESP-PCT}.

ESP-PCT: Enhanced VR Semantic Performance through Efficient Compression of Temporal and Spatial Redundancies in Point Cloud Transformers

Abstract

Semantic recognition is pivotal in virtual reality (VR) applications, enabling immersive and interactive experiences. A promising approach is utilizing millimeter-wave (mmWave) signals to generate point clouds. However, the high computational and memory demands of current mmWave point cloud models hinder their efficiency and reliability. To address this limitation, our paper introduces ESP-PCT, a novel Enhanced Semantic Performance Point Cloud Transformer with a two-stage semantic recognition framework tailored for VR applications. ESP-PCT takes advantage of the accuracy of sensory point cloud data and optimizes the semantic recognition process, where the localization and focus stages are trained jointly in an end-to-end manner. We evaluate ESP-PCT on various VR semantic recognition conditions, demonstrating substantial enhancements in recognition efficiency. Notably, ESP-PCT achieves a remarkable accuracy of 93.2% while reducing the computational requirements (FLOPs) by 76.9% and memory usage by 78.2% compared to the existing Point Transformer model simultaneously. These underscore ESP-PCT's potential in VR semantic recognition by achieving high accuracy and reducing redundancy. The code and data of this project are available at \url{https://github.com/lymei-SEU/ESP-PCT}.
Paper Structure (15 sections, 8 equations, 8 figures, 2 tables)

This paper contains 15 sections, 8 equations, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Vision Transformer
  4. Point Transformer
  5. ESP-PCT
  6. Preliminaries
  7. Localization Stage
  8. Semantic-discriminative Regions Identification
  9. Focus Stage
  10. VR Semantic Recognition
  11. Experiments
  12. Datasets
  13. Implementation
  14. Experimental Results
  15. Conclusion

Figures (8)

  • Figure 1: ESP-PCT enhances model accuracy while reducing the model's computational complexity (FLOPs) by 76.9%.
  • Figure 2: Overview of ESP-PCT. (a) ESP-PCT inputs the point cloud into a point transformer, which assigns attention scores for each point. (b) The adjacent points in the point cloud are grouped, and a collective group score is computed. When semantic resolution is unclear, the Neighborhood Global Semantic Attention (NGSA) mechanism searches semantically discriminative regions. (c) The top-K points with the highest global semantic attention are chosen for focused recognition, with the efficiency of this stage bolstered by feature fusion strategies. (d) The recognition networks take the VR semantic point cloud as input and perform further semantic recognition.
  • Figure 3: Illustration of ESP-PCT semantic-discriminative region identification. The black numbers indicate the global semantic attention of points. The yellow number indicates the region with the maximum neighborhood global semantic attention (NGSA), which is selected as the VR semantic region.
  • Figure 4: The architecture of AppNet for application recognition.
  • Figure 5: The architecture of KeyNet for keystroke recognition.
  • ...and 3 more figures