随着3D传感器技术的发展,3D点云由于其高精度而广泛应用于工业场景中,促进了点云压缩技术的发展。学习点云压缩因其优异的率失真性能而备受关注。然而,在这些方法中,模型和压缩率之间存在一一对应关系。要以不同的速率实现压缩,大量的模型需要训练,这增加了训练时间和存储空间。为了解决这个问题,提出了一种可变速率点云压缩方法,它可以在单个模型中通过超参数调整压缩率。针对变速率模型联合优化传统率失真损失时出现的窄速率范围问题,提出了一种基于对比学习的速率扩展方法来扩展模型的比特率范围。为了提高重建点云的可视化效果,引入了边界学习方法,通过边界优化提高了边界点的分类能力,提高了模型的整体性能。实验结果表明,该方法在保证模型性能的同时,实现了大码率范围的变码率压缩。所提出的方法优于G-PCC,对G-PCC实现超过70%的BD率,并执行,以及在高比特率下学习的方法。
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.