ESP-PCT: Enhanced VR Semantic Performance through Efficient Compression of Temporal and Spatial Redundancies in Point Cloud Transformers
<|reference_start|>ESP-PCT: Enhanced VR Semantic Performance through Efficient Compression of Temporal and Spatial Redundancies in Point Cloud Transformers: 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}.<|reference_end|>
@article{mei2024esp-pct:,
    title={ESP-PCT: Enhanced VR Semantic Performance through Efficient Compression
  of Temporal and Spatial Redundancies in Point Cloud Transformers},
    author={Luoyu Mei, Shuai Wang, Yun Cheng, Ruofeng Liu, Zhimeng Yin, Wenchao
  Jiang, Shuai Wang, Wei Gong},
    journal={Proceedings of the Thirty-Third International Joint Conference on
  Artificial Intelligence, IJCAI 2024},
    year={2024},
    doi={10.24963/ijcai.2024/131},
    archivePrefix={arXiv},
    eprint={2409.01216},
    primaryClass={cs.CV cs.AI}
}