4 citations · 7 across the 5 of their papers we have counts for
5 papers
FreeScale: Scaling 3D Scenes via Certainty-Aware Free-View Generation
Chenhan Jiang, Yu Chen, Qingwen Zhang +4
The development of generalizable Novel View Synthesis (NVS) models is critically limited by the scarcity of large-scale training data featuring diverse and precise camera trajector…
ZeroGS: Training 3D Gaussian Splatting from Unposed Images
Yu Chen, Rolandos Alexandros Potamias, Evangelos Ververas +3
Neural radiance fields (NeRF) and 3D Gaussian Splatting (3DGS) are popular techniques to reconstruct and render photo-realistic images. However, the pre-requisite of running Struct…
DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus
Yu Chen, Gim Hee Lee
The recent advances in 3D Gaussian Splatting (3DGS) show promising results on the novel view synthesis (NVS) task. With its superior rendering performance and high-fidelity renderi…
SCALAR-NeRF: SCAlable LARge-scale Neural Radiance Fields for Scene Reconstruction
Yu Chen, Gim Hee Lee
In this work, we introduce SCALAR-NeRF, a novel framework tailored for scalable large-scale neural scene reconstruction. We structure the neural representation as an encoder-decode…
Multi-Scale 3D Gaussian Splatting for Anti-Aliased Rendering
Zhiwen Yan, Weng Fei Low, Yu Chen +1
3D Gaussians have recently emerged as a highly efficient representation for 3D reconstruction and rendering. Despite its high rendering quality and speed at high resolutions, they…