الملخص
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.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
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اقتبس هذه المقالة
APA 7
Yao, M., Liu, J., Wang, G., Liu, H., & Li, H. (2026). Hierarchical Frequency-Domain Compression of Implicit Geometric Representations for Large-Scale Point Clouds. https://omanscience.com/ar/articles/hierarchical-frequency-domain-compression-of-implicit-geometric-representations-for-large-scale-point-clouds
MLA 9
Yao, Manlin, et al. "Hierarchical Frequency-Domain Compression of Implicit Geometric Representations for Large-Scale Point Clouds." https://omanscience.com/ar/articles/hierarchical-frequency-domain-compression-of-implicit-geometric-representations-for-large-scale-point-clouds.
شيكاغو (المؤلف–التاريخ)
Yao, Manlin, Jiabin Liu, Guan Wang, Haixu Liu, and Hui Li. 2026. "Hierarchical Frequency-Domain Compression of Implicit Geometric Representations for Large-Scale Point Clouds." https://omanscience.com/ar/articles/hierarchical-frequency-domain-compression-of-implicit-geometric-representations-for-large-scale-point-clouds.
هارفارد
Yao, M., Liu, J., Wang, G., Liu, H. and Li, H. (2026) 'Hierarchical Frequency-Domain Compression of Implicit Geometric Representations for Large-Scale Point Clouds', Available at: https://omanscience.com/ar/articles/hierarchical-frequency-domain-compression-of-implicit-geometric-representations-for-large-scale-point-clouds.
فانكوفر
Yao M, Liu J, Wang G, Liu H, Li H. Hierarchical Frequency-Domain Compression of Implicit Geometric Representations for Large-Scale Point Clouds. https://omanscience.com/ar/articles/hierarchical-frequency-domain-compression-of-implicit-geometric-representations-for-large-scale-point-clouds
IEEE
M. Yao, J. Liu, G. Wang, H. Liu, and H. Li, "Hierarchical Frequency-Domain Compression of Implicit Geometric Representations for Large-Scale Point Clouds," https://omanscience.com/ar/articles/hierarchical-frequency-domain-compression-of-implicit-geometric-representations-for-large-scale-point-clouds.