Large-scale Gaussian Splatting

PhD Qualifying Examination


Title: "Large-scale Gaussian Splatting"

by

Mr. Yunfan ZENG


Abstract:

3D Gaussian Splatting (3DGS) has emerged as an explicit scene representation 
for real-time novel view synthesis, combining ideas from point-based 
rendering and neural radiance fields to achieve high visual quality with 
efficient training and rendering. While 3DGS performs well on object-centric 
and moderate-scale scenes, extending it to large, unbounded environments 
introduces substantial challenges in memory consumption, optimization 
scalability, and rendering efficiency.

In this survey, we provide a comprehensive review of large-scale Gaussian 
splatting techniques. We first revisit the foundations of Gaussian splatting 
and its relation to neural rendering. We then systematically examine recent 
advances across representation design, including level-of-detail modeling, 
compression, hybrid primitives, and scene synthesis. Furthermore, we discuss 
scalable training strategies, GPU-oriented rendering optimizations, and 
streaming pipelines that enable deployment in large-scale settings. Finally, 
we outline open challenges and future research directions toward scalable and 
practical 3D scene representations.


Date:                   Friday, 24 April 2026

Time:                   3:30pm - 5:30pm

Venue:                  Room 2132C
                        Lift 22

Committee Members:      Prof. Pedro Sander (Supervisor)
                        Prof. Long Quan (Chairperson)
                        Dr. Dan Xu