5 papers
OctMesh: A Unified Octree-Hierarchical Framework for Lossless Triangle Mesh Compression
Shiyu Feng, Xihua Sheng, Lingyu Zhu +2
Lossless triangle mesh compression must preserve both vertex positions and connectivity. Octrees support learned point cloud geometry coding and progressive refinement, but extendi…
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…
SPC-NeRF: Spatial Predictive Compression for Voxel Based Radiance Field
Zetian Song, Wenhong Duan, Yuhuai Zhang +3
Representing the Neural Radiance Field (NeRF) with the explicit voxel grid (EVG) is a promising direction for improving NeRFs. However, the EVG representation is not efficient for…