The past several years have witnessed the emergence of learned point cloud
compression (PCC) techniques. However, current learning-based lossless point
cloud attribute compression (PCAC) methods either suffer from high
computational complexity or deteriorated compression performance. Moreover, the
significant variations in point cloud scale and sparsity encountered in
real-world applications make developing an all-in-one neural model a
challenging task. In this paper, we propose PoLoPCAC, an efficient and generic
lossless PCAC method that achieves high compression efficiency and strong
generalizability simultaneously. We formulate lossless PCAC as the task of
inferring explicit distributions of attributes from group-wise autoregressive
priors. A progressive random grouping strategy is first devised to efficiently
resolve the point cloud into groups, and then the attributes of each group are
modeled sequentially from accumulated antecedents. A locality-aware attention
mechanism is utilized to exploit prior knowledge from context windows in
parallel. Since our method directly operates on points, it can naturally avoids
distortion caused by voxelization, and can be executed on point clouds with
arbitrary scale and density. Experiments show that our method can be instantly
deployed once trained on a Synthetic 2k-ShapeNet dataset while enjoying
continuous bit-rate reduction over the latest G-PCCv23 on various datasets
(ShapeNet, ScanNet, MVUB, 8iVFB). Meanwhile, our method reports shorter coding
time than G-PCCv23 on the majority of sequences with a lightweight model size
(2.6MB), which is highly attractive for practical applications. Dataset, code
and trained model are available at
this https URL

在这篇论文中，我们提出了 PoLoPCAC，一种高效且通用的无损点云属性压缩方法，它在高压缩效率和强大的泛化能力方面取得了平衡。我们通过将无损 PCAC 建模为从群组先验中推断属性的显式分布的任务来实现压缩。我们的方法直接在点上操作，可以自然地避免由于体素化而引起的扭曲，并且可以在任意尺度和密度的点云上执行。实验证明，我们的方法在各种数据集上（ShapeNet、ScanNet、MVUB、8iVFB）比最新的 G-PCCv23 具有持续的比特率降低，并且比 G-PCCv23 在大多数序列上具有更短的编码时间和轻量级模型尺寸（2.6MB），这对于实际应用非常有吸引力。