The School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
The School of of Computer Science and Technology, Donghua University, Shanghai 201620, China.
Sensors (Basel). 2023 Jun 9;23(12):5474. doi: 10.3390/s23125474.
With the development of 3D sensors technology, 3D point cloud is widely used in industrial scenes due to their high accuracy, which promotes the development of point cloud compression technology. Learned point cloud compression has attracted much attention for its excellent rate distortion performance. However, there is a one-to-one correspondence between the model and the compression rate in these methods. To achieve compression at different rates, a large number of models need to be trained, which increases the training time and storage space. To address this problem, a variable rate point cloud compression method is proposed, which enables the adjustment of the compression rate by the hyperparameter in a single model. To address the narrow rate range problem that occurs when the traditional rate distortion loss is jointly optimized for variable rate models, a rate expansion method based on contrastive learning is proposed to expands the bit rate range of the model. To improve the visualization effect of the reconstructed point cloud, a boundary learning method is introduced to improve the classification ability of the boundary points through boundary optimization and enhance the overall model performance. The experimental results show that the proposed method achieves variable rate compression with a large bit rate range while ensuring the model performance. The proposed method outperforms G-PCC, achieving more than 70% BD-Rate against G-PCC, and performs about, as well as the learned methods at high bit rates.
随着 3D 传感器技术的发展，3D 点云由于其高精度而在工业场景中得到了广泛应用，这促进了点云压缩技术的发展。基于学习的点云压缩由于其出色的率失真性能而受到了广泛关注。然而，这些方法中的模型和压缩率之间存在一一对应的关系。为了实现不同的压缩率，需要训练大量的模型，这增加了训练时间和存储空间。针对这个问题，提出了一种可变率点云压缩方法，该方法可以通过单个模型中的超参数来调整压缩率。为了解决传统的率失真损失在联合优化可变率模型时出现的窄率范围问题，提出了一种基于对比学习的率扩展方法，以扩展模型的比特率范围。为了提高重构点云的可视化效果，引入了边界学习方法，通过边界优化提高边界点的分类能力，增强整体模型性能。实验结果表明，所提出的方法在保证模型性能的同时实现了可变率压缩，并具有较大的比特率范围。与 G-PCC 相比，所提出的方法的 BD-Rate 超过 70%，在高比特率下的性能与基于学习的方法相当。
