1 citations · 1 across the 5 of their papers we have counts for
5 papers
DALD-PCAC: Density-Adaptive Learning Descriptor for Point Cloud Lossless Attribute Compression
Chunyang Fu, Ge Li, Wei Gao +3
Recently, deep learning has significantly advanced the performance of point cloud geometry compression. However, the learning-based lossless attribute compression of point clouds w…
DeepRAHT: Learning Predictive RAHT for Point Cloud Attribute Compression
Chunyang Fu, Tai Qin, Shiqi Wang +1
Regional Adaptive Hierarchical Transform (RAHT) is an effective point cloud attribute compression (PCAC) method. However, its application in deep learning lacks research. In this p…
Voxel-GS: Quantized Scaffold Gaussian Splatting Compression with Run-Length Coding
Chunyang Fu, Xiangrui Liu, Shiqi Wang +1
Substantial Gaussian splatting format point clouds require effective compression. In this paper, we propose Voxel-GS, a simple yet highly effective framework that departs from the…
CompGS++: Compressed Gaussian Splatting for Static and Dynamic Scene Representation
Xiangrui Liu, Xinju Wu, Shiqi Wang +2
Gaussian splatting demonstrates proficiency for 3D scene modeling but suffers from substantial data volume due to inherent primitive redundancy. To enable future photorealistic 3D…
CompGS: Efficient 3D Scene Representation via Compressed Gaussian Splatting
Xiangrui Liu, Xinju Wu, Pingping Zhang +3
Gaussian splatting, renowned for its exceptional rendering quality and efficiency, has emerged as a prominent technique in 3D scene representation. However, the substantial data vo…