AI 中文总结
针对大规模点云压缩成本高的问题，提出统一框架，结合隐式几何场、分层频域压缩与条件高频预测，在超八百万点复杂点云上实现更高压缩比且保持重建质量。
AI 中文摘要
复杂几何的大规模点云表示会产生高昂的计算和内存成本，因此需要压缩的隐式表示。为解决这一问题，我们提出了一个统一框架，包括隐式几何场表示、分层频域压缩和条件高频预测。具体而言，无序点云被映射到其物理包围盒内定义的隐式场中。随后构建一个平滑的傅里叶金字塔，其中紧凑的低频分量捕获全局几何。跨尺度高频残差被编码以保留重建精细几何细节所需的空间信息。为恢复压缩过程中丢失的高频信息，我们开发了一个分层3D神经网络。重建的隐式场通过等值面提取转换回点云。在包含超过八百万个点的复杂边界点云上的实验表明，所提出的方法在保持相当重建质量的同时，实现了比现有点云压缩方法更高的压缩比。
英文摘要
Large-scale point cloud representations of complex geome tries incur prohibitive computational and memory costs, necessitating compressed implicit representations. To ad dress this, we propose a unified framework comprising im plicit geometric field representation, hierarchical frequency domain compression, and conditional high-frequency predic tion. Specifically, an unordered point cloud is mapped to an implicit field defined within its physical bounding box. A smooth Fourier pyramid is then constructed, where com pact low-frequency components capture the global geometry. Inter-scale high-frequency residuals are encoded to preserve the spatial information required for reconstructing fine geo metric details. To restore the high-frequency information lost during compression, we develop a hierarchical 3D neural net work. The reconstructed implicit field is converted back into a point cloud through isosurface extraction. Experiments on a complex-boundary point cloud with more than eight mil lion points demonstrate that the proposed method achieves a higher compression ratio than existing point cloud compres sion methods while maintaining comparable reconstruction quality.
