Rong Liu (柳荣)

Research Engineer

USC Institute for Creative Technologies

About Me

I’m Rong Liu, a Research Engineer at the USC Institute for Creative Technologies.

My long-term research goal is to answer how we enable machines to understand, reconstruct, and generate our everyday interactive and dynamic world through computer vision, graphics, and machine learning.

Current research interests: 3D/4D Reconstruction and Generation, Neural Representation and Rendering, and World Modeling.

Education

University of Southern California

August 2022 - May 2024

Master of Science in Computer Science (Honors Merit)

August 2022 - May 2024

GPA: 4.0/4.0

Dalian University of Technology

September 2018 - June 2022

Bachelor of Engineering in Computer Engineering

September 2018 - June 2022

GPA: 88/100

Publications

Universal Beta Splatting

Rong Liu, Zhongpai Gao, Benjamin Planche, Meida Chen, Van Nguyen Nguyen, Meng Zheng, Anwesa Choudhuri, Terrence Chen, Yue Wang, Andrew Feng, Ziyan Wu

ICLR 2026

Universal Beta Splatting generalizes 3D Gaussian Splatting to N-dimensional anisotropic Beta kernels, enabling controllable dependency modeling across spatial, angular, and temporal dimensions within a single representation.

Splat Feature Solver

Butian Xiong, Rong Liu, Kenneth Xu, Meida Chen, Andrew Feng

ICLR 2026

A unified, kernel- and feature-agnostic framework that formulates feature lifting as a sparse linear inverse problem, enabling efficient closed-form solutions with high-quality 3D semantic features.

Deformable Beta Splatting

Rong Liu*, Dylan Sun*, Meida Chen, Yue Wang†, Andrew Feng†

SIGGRAPH 2025

Deformable Beta Splatting introduces deformable Beta Kernels with adaptive frequency control for both geometry and color encoding, capturing complex geometries and lighting while only using 45% parameters and rendering 1.5x faster than 3DGS-MCMC.

SplatMAP: Online Dense Monocular SLAM with 3D Gaussian Splatting

Yue Hu, Rong Liu, Meida Chen, Peter Beerel, Andrew Feng

I3D 2025

A real-time monocular SLAM system that fuses 3D Gaussian Splatting with SLAM’s dynamic depth and pose updates via SLAM-Informed Adaptive Densification and Geometry-Guided Optimization.

AtomGS: Atomizing Gaussian Splatting for High-Fidelity Radiance Field

Rong Liu, Rui Xu, Yue Hu, Meida Chen, Andrew Feng

BMVC 2024

AtomGS proposes an atomized proliferation of Gaussians and edge-aware normal loss to refine Gaussian splatting, boosting geometric precision and rendering fidelity in novel-view synthesis.

UAV swarm based radar signal sorting via multi-source data fusion: A deep transfer learning framework

Liangtian Wan, Rong Liu, Lu Sun, Hansong Nie, Xianpeng Wang

Information Fusion. 78 (2022): 90-101

A UAV-swarm–enabled deep transfer learning framework that fuses radar pulses across time and space to accurately classify and sort complex radar signals.

Preprints

Universal Photorealistic Style Transfer: A Lightweight and Adaptive Approach

Rong Liu, Enyu Zhao, Zhiyuan Liu, Andrew Feng, Scott John Easley

arXiv:2309.10011

A lightweight style transfer network that learns instance-adaptive photorealistic transfer on-the-fly and scales effortlessly to high-resolution images and videos.

Experiences

Research Engineer

May 2024 - Present

Supervisor: Prof. Andrew Feng

USC Institute for Creative Technologies

Supervisor: Prof. Andrew Feng

May 2024 - Present

  • Deformable Beta Splatting
  • SplatMAP: Online Dense Monocular SLAM with 3D Gaussian Splatting
  • Splat Feature Solver

Research Intern

June 2025 - August 2025

Supervisor: Dr. Zhongpai Gao

United Imaging Intelligence

Supervisor: Dr. Zhongpai Gao

June 2025 - August 2025

  • Universal Beta Splatting
  • Radiance fields on medical data

Research Assistant

May 2023 - May 2024

Supervisor: Prof. Andrew Feng

USC Institute for Creative Technologies

Supervisor: Prof. Andrew Feng

May 2023 - May 2024

  • Neural Radiance Fields (NeRF)
  • Scalable NeRF on large terrain datasets
  • 3D Gaussian Splatting
  • Atomizing Gaussian Splatting for High-Fidelity Radiance Field
  • Mesh extraction from Radiance Field

Research Assistant

July 2020 - July 2022

Supervisor: Prof. Liangtian Wan

Dalian University of Technology

Supervisor: Prof. Liangtian Wan

July 2020 - July 2022

  • Objective Detection (RCNN and YOLO)
  • Transfer Learning

Teaching Assistant

September 2021 - June 2022

Dalian University of Technology

September 2021 - June 2022

  • Program Design Basics and C Programming
  • Object-Oriented Method and C++ Program Design

Awards

  • Computer Science Master’s Student Honors Merit - University of Southern California (USC), 2024

  • DUT Outstanding Undergraduate Thesis - Dalian University of Technology (DUT), 2022

  • First Prize in the National Artificial Intelligence Knowledge Competition for College Students - JiangSu Association of Artificial Intelligence (JSAI), 2020

  • Meritorious Winner in Mathematical Contest in Modeling - Consortium for Mathematics and Its Applications (COMAP), 2019

Service