School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China.
Sensors (Basel). 2022 Feb 7;22(3):1262. doi: 10.3390/s22031262.
As a kind of information-intensive 3D representation, point cloud rapidly develops in immersive applications, which has also sparked new attention in point cloud compression. The most popular dynamic methods ignore the characteristics of point clouds and use an exhaustive neighborhood search, which seriously impacts the encoder's runtime. Therefore, we propose an improved compression means for dynamic point cloud based on curvature estimation and hierarchical strategy to meet the demands in real-world scenarios. This method includes initial segmentation derived from the similarity between normals, curvature-based hierarchical refining process for iterating, and image generation and video compression technology based on de-redundancy without performance loss. The curvature-based hierarchical refining module divides the voxel point cloud into high-curvature points and low-curvature points and optimizes the initial clusters hierarchically. The experimental results show that our method achieved improved compression performance and faster runtime than traditional video-based dynamic point cloud compression.
作为一种信息密集型的 3D 表示形式，点云在沉浸式应用中迅速发展，这也引发了对点云压缩的新关注。最流行的动态方法忽略了点云的特征，使用详尽的邻域搜索，这严重影响了编码器的运行时。因此，我们提出了一种基于曲率估计和分层策略的动态点云改进压缩方法，以满足实际场景中的需求。该方法包括基于法向量相似性的初始分割、基于曲率的分层细化迭代过程，以及基于去冗余的图像生成和视频压缩技术，不会造成性能损失。基于曲率的分层细化模块将体素点云分为高曲率点和低曲率点，并分层优化初始聚类。实验结果表明，与传统基于视频的动态点云压缩方法相比，我们的方法实现了更好的压缩性能和更快的运行时。
