Efficient and Generic Point Model for Lossless Point Cloud Attribute
  Compression
Kang You Pan Gao Zhan Ma
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
https://github.com/I2-Multimedia-Lab/PoLoPCAC.