The paper proposes PoLoPCAC, an efficient and generic lossless point cloud attribute compression (PCAC) method. The key highlights are:
PoLoPCAC formulates lossless PCAC as the task of inferring explicit distributions of attributes from group-wise autoregressive priors. It uses a progressive random grouping strategy to efficiently resolve the point cloud into groups, and then models the attributes of each group sequentially from accumulated antecedents.
A locality-aware attention mechanism is utilized to exploit prior knowledge from context windows in parallel for efficient attribute coding within each group.
PoLoPCAC directly operates on points, avoiding distortion caused by voxelization, and can be executed on point clouds with arbitrary scale and density without requiring any prior knowledge of the test domain.
Experiments show that PoLoPCAC can be instantly deployed once trained on a small Synthetic 2k-ShapeNet dataset, while enjoying continuous bit-rate reduction over the latest G-PCCv23 on various datasets. It also reports shorter coding time than G-PCCv23 on the majority of sequences with a lightweight model size.
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by Kang You,Pan... às arxiv.org 04-11-2024
https://arxiv.org/pdf/2404.06936.pdfPerguntas Mais Profundas