15 citations · 41 across the 16 of their papers we have counts for
16 papers
Honey, I Shrunk the Arc de Triomphe!
Yuanbo Xiangli, Hanyu Chen, Xueqing Tsang +1
Metric scale monocular geometry estimation has seen significant progress through large-scale data aggregation, yet current foundation models suffer from a persistent ''scale-collap…
Long-tail Internet photo reconstruction
Yuan Li, Yuanbo Xiangli, Hadar Averbuch-Elor +2
Internet photo collections exhibit an extremely long-tailed distribution: a few famous landmarks are densely photographed and easily reconstructed in 3D, while most real-world site…
Proc-GS: Procedural Building Generation for City Assembly with 3D Gaussians
Yixuan Li, Xingjian Ran, Linning Xu +6
Buildings are primary components of cities, often featuring repeated elements such as windows and doors. Traditional 3D building asset creation is labor-intensive and requires spec…
Turbo-GS: Accelerating 3D Gaussian Fitting for High-Quality Radiance Fields
Ankit Dhiman, Tao Lu, R Srinath +5
Novel-view synthesis plays a crucial role in computer vision with applications in 3D reconstruction, mixed reality, and robotics. Recent approaches, such as 3D Gaussian Splatting (…
Doppelgangers++: Improved Visual Disambiguation with Geometric 3D Features
Yuanbo Xiangli, Ruojin Cai, Hanyu Chen +2
Accurate 3D reconstruction is frequently hindered by visual aliasing, where visually similar but distinct surfaces (aka, doppelgangers), are incorrectly matched. These spurious mat…
Neural Gaffer: Relighting Any Object via Diffusion
Haian Jin, Yuan Li, Fujun Luan +6
Single-image relighting is a challenging task that involves reasoning about the complex interplay between geometry, materials, and lighting. Many prior methods either support only…
GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting
Kai Zhang, Sai Bi, Hao Tan +4
We propose GS-LRM, a scalable large reconstruction model that can predict high-quality 3D Gaussian primitives from 2-4 posed sparse images in 0.23 seconds on single A100 GPU. Our m…
GSDF: 3DGS Meets SDF for Improved Rendering and Reconstruction
Mulin Yu, Tao Lu, Linning Xu +3
Presenting a 3D scene from multiview images remains a core and long-standing challenge in computer vision and computer graphics. Two main requirements lie in rendering and reconstr…
Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering
Tao Lu, Mulin Yu, Linning Xu +4
Neural rendering methods have significantly advanced photo-realistic 3D scene rendering in various academic and industrial applications. The recent 3D Gaussian Splatting method has…
MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyond
Yixuan Li, Lihan Jiang, Linning Xu +4
Neural radiance fields (NeRF) and its subsequent variants have led to remarkable progress in neural rendering. While most of recent neural rendering works focus on objects and smal…
Grid-guided Neural Radiance Fields for Large Urban Scenes
Linning Xu, Yuanbo Xiangli, Sida Peng +5
Purely MLP-based neural radiance fields (NeRF-based methods) often suffer from underfitting with blurred renderings on large-scale scenes due to limited model capacity. Recent appr…
AssetField: Assets Mining and Reconfiguration in Ground Feature Plane Representation
Yuanbo Xiangli, Linning Xu, Xingang Pan +3
Both indoor and outdoor environments are inherently structured and repetitive. Traditional modeling pipelines keep an asset library storing unique object templates, which is both v…
OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images
Weijia Li, Yawen Lai, Linning Xu +5
This paper presents OmniCity, a new dataset for omnipotent city understanding from multi-level and multi-view images. More precisely, the OmniCity contains multi-view satellite ima…
BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering
Yuanbo Xiangli, Linning Xu, Xingang Pan +5
Neural radiance fields (NeRF) has achieved outstanding performance in modeling 3D objects and controlled scenes, usually under a single scale. In this work, we focus on multi-scale…
Real or Not Real, that is the Question
Yuanbo Xiangli, Yubin Deng, Bo Dai +2
While generative adversarial networks (GAN) have been widely adopted in various topics, in this paper we generalize the standard GAN to a new perspective by treating realness as a…
Nowhere to Hide: Cross-modal Identity Leakage between Biometrics and Devices
Chris Xiaoxuan Lu, Yang Li, Yuanbo Xiangli +1
Along with the benefits of Internet of Things (IoT) come potential privacy risks, since billions of the connected devices are granted permission to track information about their us…