点云
计算机科学
有损压缩
残余物
失真(音乐)
解码方法
压缩(物理)
算法
数据压缩
计算科学
比例(比率)
人工智能
计算机视觉
电信
量子力学
复合材料
放大器
材料科学
物理
带宽(计算)
作者
Pengpeng Yu,Dongzhou Zuo,Yang Huang,Ruishan Huang,Hanyun Wang,Yulan Guo,Fan Li
标识
DOI:10.1109/icme55011.2023.00435
摘要
The increasing applications of 3D point clouds require efficient compression techniques to achieve high-quality and low-delay services. However, the computational efficiency and rate-distortion performance for large-scale dense point clouds are still challenging, and the phenomenon of reconstruction ability degradation also exists when the network is deep. To solve these challenges, we propose a novel fully end-to-end point cloud compression model based on sparse convolution. Specifically, we adopt a long-range-residual aided architecture to avoid the reconstruction degradation and high computational complexity of deep networks. Further, we propose a multi-scale geometry compression module to construct an end-to-end network that avoids the accumulation of reconstruction distortion during decoding. Experiments on the large-scale Moving Picture Experts Group (MPEG) PCC benchmarks show that our model outperforms the latest Video-based Point Cloud Compression (V-PCC) scheme in terms of lossy geometry compression by 50.4% in D1 BD-rate and 50.8% in D2 BD-rate, while maintaining affordable processing speed and memory consumption.
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