5 papers
AnyPcc: Compressing Any Point Cloud with a Single Universal Model
Kangli Wang, Qianxi Yi, Yuqi Ye +2
Generalization remains a critical challenge in deep learning-based point cloud geometry compression. While existing methods perform well on standard benchmarks, their performance c…
Generalized Gaussian Entropy Model for Point Cloud Attribute Compression with Dynamic Likelihood Intervals
Changhao Peng, Yuqi Ye, Wei Gao
Gaussian and Laplacian entropy models are proved effective in learned point cloud attribute compression, as they assist in arithmetic coding of latents. However, we demonstrate thr…
A Novel Benchmark and Dataset for Efficient 3D Gaussian Splatting with Gaussian Point Cloud Compression
Kangli Wang, Shihao Li, Qianxi Yi +1
Recently, immersive media and autonomous driving applications have significantly advanced through 3D Gaussian Splatting (3DGS), which offers high-fidelity rendering and computation…
UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach
Kangli Wang, Wei Gao
Learning-based point cloud compression methods have made significant progress in terms of performance. However, these methods still encounter challenges including high complexity,…
LLM-PCGC: Large Language Model-based Point Cloud Geometry Compression
Yuqi Ye, Wei Gao
The key to effective point cloud compression is to obtain a robust context model consistent with complex 3D data structures. Recently, the advancement of large language models (LLM…