Kavli Affiliate: Wei Gao
| First 5 Authors: Kangli Wang, Kangli Wang, , ,
| Summary:
Generalization remains a critical challenge for deep learning-based point
cloud geometry compression. We argue this stems from two key limitations: the
lack of robust context models and the inefficient handling of
out-of-distribution (OOD) data. To address both, we introduce AnyPcc, a
universal point cloud compression framework. AnyPcc first employs a Universal
Context Model that leverages priors from both spatial and channel-wise grouping
to capture robust contextual dependencies. Second, our novel Instance-Adaptive
Fine-Tuning (IAFT) strategy tackles OOD data by synergizing explicit and
implicit compression paradigms. It fine-tunes a small subset of network weights
for each instance and incorporates them into the bitstream, where the marginal
bit cost of the weights is dwarfed by the resulting savings in geometry
compression. Extensive experiments on a benchmark of 15 diverse datasets
confirm that AnyPcc sets a new state-of-the-art in point cloud compression. Our
code and datasets will be released to encourage reproducible research.
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