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Zhu Li

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • eess.IV2
  • cs.GR1
  • cs.MM1
same name
  • Zhu Li — 14 papers, h 29
  • Zhu Li — 11 papers, h 7
  • Zhu Li — 7 papers
  • Zhu Li — 4 papers
  • Zhu Li — 3 papers
  • Zhu Li — 2 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedCompGS++: Compressed Gaussian Splatting for Static and Dynamic Scene Representation

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

eess.IV2026

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…

eess.IV2026

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…

cs.MM2025

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…

cs.GR2025★ 1 cited

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.