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Qizhe Gao

dblp:412/9012 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0007-3751-8320ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Efficient and distributed learning · 67% 3D vision · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › neural rendering
3d gaussian splatting
0.912025
FlexGaussian: Flexible and Cost-Effective Training-Free Compression for 3D Gaussian Splatting · ACM Multimedia 2025
Machine learning › Efficient and distributed learning
compression
0.912025
FlexGaussian: Flexible and Cost-Effective Training-Free Compression for 3D Gaussian Splatting · ACM Multimedia 2025
Machine learning › Efficient and distributed learning
model compression
0.912025
FlexGaussian: Flexible and Cost-Effective Training-Free Compression for 3D Gaussian Splatting · ACM Multimedia 2025

Methods — techniques the papers use, named apart from their topics

quantization · 0.9pruning · 0.9
YearPublicationVenuePosition
2025 FlexGaussian: Flexible and Cost-Effective Training-Free Compression for 3D Gaussian Splatting
abstract
3D Gaussian Splatting has emerged as a prominent technique for representing and rendering complex 3D scenes, offering high fidelity and speed but resulting in large file sizes. Existing compression methods can reduce 3D Gaussian data size but often require costly retraining or refinement, which is memory- and compute-intensive and lacks flexibility for varying compression needs. This challenge grows as large-scale scenes become more common, increasing the demand for efficient, low-overhead compression methods - especially for resource-constrained mobile and edge devices.
Boyuan Tian, Qizhe Gao, Siran Xianyu, Xiaotong Cui, Minjia Zhang
ACM Multimedia2