一个模型搞定所有点云压缩，真实场景表现更优
AnyPcc: Compressing Any Point Cloud with a Single Universal Model
- 用通用上下文模型融合粗细空间先验，适应各种密度点云
- 实例自适应微调仅传少量权重，压缩率提升明显
- 适合需要通用性与低复杂度的点云应用
基于深度学习的点云几何压缩在标准基准上表现良好，但在真实场景中因两大瓶颈而性能下降：缺乏对不同数据密度鲁棒的上下文模型，以及无法有效适应分布外（OOD）数据。为此，我们提出AnyPcc，一种通用点云压缩框架。首先，采用通用上下文模型，结合粗粒度空间先验与细粒度通道先验，实现全密度范围的鲁棒建模。其次，提出创新的实例自适应微调（IAFT）策略，通过显式与隐式压缩范式的协同，对每个实例微调部分网络权重，并将这些权重嵌入比特流传输。少量比特开销远低于压缩性能增益。在15个多样化数据集上的大量实验表明，AnyPcc在保持低复杂度的同时，达到点云压缩新基准。
原文摘要 · Abstract (English)
Generalization remains a critical challenge in deep learning-based point cloud geometry compression. While existing methods perform well on standard benchmarks, their performance collapses in real-world scenarios due to two fundamental limitations: the lack of context models that are robust across diverse data densities, and the inability to efficiently adapt to out-of-distribution (OOD) data. To overcome both challenges, we introduce AnyPcc, a universal point cloud compression framework. AnyPcc first employs a Universal Context Model that leverages coarse-grained spatial priors with fine-grained channel priors to ensure robust context modeling across the entire density spectrum. Second, our novel Instance-Adaptive Fine-Tuning (IAFT) strategy tackles OOD data by synergizing explicit and implicit compression paradigms. For each instance, it fine-tunes a small subset of network weights and transmits them within the bitstream. The minimal bitrate overhead from these weights is significantly outweighed by the resulting gains in geometry compression. Extensive experiments on a benchmark of 15 diverse datasets confirm that AnyPcc sets a new state-of-the-art in point cloud compression while maintaining low complexity. Our code and datasets have been released to encourage reproducible research.
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