用分层注意力提升点云属性无损压缩效率与质量
Hierarchical Attention Networks for Lossless Point Cloud Attribute Compression
- 构建多分辨率层次结构，逐级细化点云特征
- 在颜色与反射率上优于最新G-PCC，且编码解码更快
- 适合需要高效无损压缩的三维点云应用
本文提出一种基于深度分层注意力的点云属性无损压缩方法，利用多分辨率空间结构和残差学习。引入简洁有效的细节层级（LoD）结构，实现从粗到精的表示。同一细化层级内的点并行编码，共享同一上下文点组，通过分层聚合邻近点信息，捕捉跨尺度与密度的上下文依赖关系，实现全面特征提取。对位置坐标和属性进行归一化处理，实现尺度不变压缩。同时将点云分割为多个切片，支持并行处理，进一步优化时间复杂度。实验表明，该方法在颜色与反射率属性上的编码性能优于最新G-PCC标准，同时保持更高效的编码与解码运行时。
原文摘要 · Abstract (English)
In this paper, we propose a deep hierarchical attention context model for lossless attribute compression of point clouds, leveraging a multi-resolution spatial structure and residual learning. A simple and effective Level of Detail (LoD) structure is introduced to yield a coarse-to-fine representation. To enhance efficiency, points within the same refinement level are encoded in parallel, sharing a common context point group. By hierarchically aggregating information from neighboring points, our attention model learns contextual dependencies across varying scales and densities, enabling comprehensive feature extraction. We also adopt normalization for position coordinates and attributes to achieve scale-invariant compression. Additionally, we segment the point cloud into multiple slices to facilitate parallel processing, further optimizing time complexity. Experimental results demonstrate that the proposed method offers better coding performance than the latest G-PCC for color and reflectance attributes while maintaining more efficient encoding and decoding runtimes.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。