针对点云密度变化难题，提出自适应压缩框架提升属性无损压缩效果。
DALD-PCAC: Density-Adaptive Learning Descriptor for Point Cloud Lossless Attribute Compression
- 基于细节层级与置换不变注意力模型建模点云稀疏不规则特性。
- 引入自适应描述符在多尺度邻域内捕捉点间结构关联，压缩率领先。
- 适合处理激光雷达和物体点云，兼顾性能与计算开销，适合实际部署。
近年来，深度学习显著提升了点云几何压缩性能，但针对不同密度的点云属性无损压缩仍研究不足。本文提出一种基于学习的框架DALD-PCAC，利用细节层级（LoD）适配点云无损属性压缩。设计了基于置换不变Transformer的点级注意力模型，以应对点云稀疏性和不规则性带来的上下文建模挑战。提出密度自适应学习描述符（DALD），可在大范围邻域内捕获点间的结构与相关性。此外，设计先验引导的块划分方法，降低块内属性方差，提升压缩性能。在激光雷达与物体点云数据集上的实验表明，DALD-PCAC在多数数据上达到当前最优表现，压缩性能显著提升且对点云密度变化具有鲁棒性。同时，在性能与复杂度间保持良好平衡，具备实际应用潜力。源代码已开源：https://github.com/zb12138/DALD_PCAC。
原文摘要 · Abstract (English)
Recently, deep learning has significantly advanced the performance of point cloud geometry compression. However, the learning-based lossless attribute compression of point clouds with varying densities is under-explored. In this paper, we develop a learning-based framework, namely DALD-PCAC that leverages Levels of Detail (LoD) to tailor for point cloud lossless attribute compression. We develop a point-wise attention model using a permutation-invariant Transformer to tackle the challenges of sparsity and irregularity of point clouds during context modeling. We also propose a Density-Adaptive Learning Descriptor (DALD) capable of capturing structure and correlations among points across a large range of neighbors. In addition, we develop a prior-guided block partitioning to reduce the attribute variance within blocks and enhance the performance. Experiments on LiDAR and object point clouds show that DALD-PCAC achieves the state-of-the-art performance on most data. Our method boosts the compression performance and is robust to the varying densities of point clouds. Moreover, it guarantees a good trade-off between performance and complexity, exhibiting great potential in real-world applications. The source code is available at https://github.com/zb12138/DALD_PCAC.
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