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Improving Occupancy Prediction for Multiscale Point Cloud Geometry Compression
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AI
DOI：10.1109/TCSVT.2025.3642141.png)
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Abstract
En 中文
Multiscale sparse representation offers significant advantages in point cloud geometry compression, delivering state-of-the-art performance compared to both standardized solutions and other learned approaches. A crucial component of this framework is the cross-scale occupancy prediction, which employs the lower-scale reference representation either from the current frame alone or from both the current and temporal reference frames to establish conditional priors for either static or dynamic coding. However, existing works mainly use local computations, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i>, sparse convolutions and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN attention, to exploit correlations in such a representation; these methods usually fail to adequately capture global coherence. In addition, the fixed configuration of lossless-lossy scales cannot adapt to temporal dynamics, which limits the reconstruction quality of temporal references in dynamic coding. These limitations constrain the generation of more effective priors used for conditional coding. To address these issues, we propose two new techniques. The first is KPA (Key Point-driven Attention), which integrates both local and global characteristics. The second is AdaScale (Adaptive Lossy/Lossless Scale), which decides whether the transitional scale should be in lossless or lossy mode based on temporal displacement, thereby enhancing the reconstruction quality of the temporal reference. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods, including rules-based standard codecs like G-PCC and V-PCC, as well as learning-based approaches like Unicorn and TMAP, across both static/dynamic and lossy/lossless coding scenarios.
Keywords:
Point cloud geometry compression
key point
cross attention
conditional coding
Journal
IF:
11.1
Papers:
612
Citations:
3.1W