Research
My research interest lies in 3D computer vision and deep
generative modeling. Currently, I am working on
photorealistic and efficient city scenes reconstruction,
manipulation and generation based on multi-source data,
including satellite imagery, oblique photography, street
view panoramas and urban planning information.
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Doppelgangers++: Improved Visual Disambiguation with
Geometric 3D Features
Yuanbo Xiangli,
Ruojin Cai, Hanyu Chen,
Jeffrey Byrne,
Noah Snavely
CVPR, 2025
project page
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paper
An enhanced pairwise image classifier that tackles visual
aliasing (doppelgangers) to improve 3D reconstruction
accuracy across diverse, real-world scenes.
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GSDF: 3DGS Meets SDF for Improved Rendering and
Reconstruction
Mulin Yu*, Tao Lu*,
Linning Xu,
Lihan Jiang,
Yuanbo Xiangli ✉️,
Bo Dai
NeurIPS, 2024
project page
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paper
A dual-branch system enhances rendering and reconstruction
at the same time, with the mutual geometry regularization
and guidance between Gaussain primitives and neural
surface.
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Neural Gaffer: Relighting Any Object via Diffusion
Haian Jin,
Yuan Li,
Fujun Luan,
Yuanbo Xiangli,
Sai Bi,
Kai Zhang,
Zexiang Xu,
Jin Sun,
Noah Snavely
NeurIPS, 2024
project page
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paper
Neural Gaffer is an end-to-end 2D relighting diffusion
model that accurately relights any object in a single
image under various lighting conditions.
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GS-LRM: Large Reconstruction Model for 3D Gaussian
Splatting
Kai Zhang*,
Sai Bi*,
Hao Tan*,
Yuanbo Xiangli,
Nanxuan Zhao,
Kalyan Sunkavalli,
Zexiang Xu
ECCV, 2024
project page
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paper
High-quality 3D Gaussian primitives from 2-4 posed sparse
images within 0.23 seconds.
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Scaffold-GS: Structured 3D Gaussians for View-Adaptive
Rendering
Tao Lu*,
Mulin Yu*,
Linning Xu,
Yuanbo Xiangli,
Limin Wang,
Dahua Lin,
Bo Dai
CVPR, 2024
project page
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paper
Scaffold-GS uses anchor points to distribute local 3D
Gaussians, and predicts their attributes on-the-fly based
on viewing direction and distance within the view frustum.
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AssetField: Assets Mining and Reconfiguration in Ground
Feature Plane Representation
Yuanbo Xiangli*,
Linning Xu*,
Xingang Pan,
Nanxuan Zhao,
Bo Dai,
Dahua Lin
ICCV, 2023
project page
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paper
A novel neural scene representation that learns a set of
object-aware ground feature planes, where an asset library
storing template feature patches can be constructed in an
unsupervised manner.
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MatrixCity: A Large-scale City Dataset for City-scale
Neural Rendering and Beyond
Yixuan Li,
Lihan Jiang,
Linning Xu,
Yuanbo Xiangli,
Dahua Lin,
Bo Dai
ICCV, 2023
project page
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paper
A large-scale, comprehensive, and high-quality synthetic
dataset for city-scale neural rendering researches.
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Grid-guided Neural Radiance Fields for Large Urban
Scenes
Linning Xu*,
Yuanbo Xiangli*,
Sida Peng,
Xingang Pan,
Nanxuan Zhao,
Christian Theobalt, Bo Dai,
Dahua Lin
CVPR, 2023
project page
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paper
We use grid features to profile the scene and a
light-weighted NeRF to pick up details. The two-branch
model can produce photo-realistic results with high
rendering speed.
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OmniCity: Omnipotent City Understanding with Multi-level
and Multi-view Images
Weijia Li,
Yawen Lai,
Linning Xu,
Yuanbo Xiangli, Jinhua Yu,
Conghui He,
Guisong Xia,
Dahua Lin
CVPR, 2023
project page
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paper
A new dataset containing multi-view satellite images and
street-level panoramas, constituting over 100K pixel-wise
annotated images that are well-aligned and collected from
25K geo-locations.
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BungeeNeRF: Progressive Neural Radiance Field for
Extreme Multi-scale Scene Rendering
Yuanbo Xiangli*,
Linning Xu*,
Xingang Pan,
Nanxuan Zhao,
Anyi Rao,
Christian Theobalt, Bo Dai,
Dahua Lin
ECCV, 2022
project page
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paper
An attempt to bring NeRF to potentially city-scale scenes,
which requires rendering drastically varied observations
(level-of-detail and spatial coverage) at multiscales.
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BlockPlanner: City Block Generation with Vectorized
Graph Representation
Linning Xu*,
Yuanbo Xiangli*,
Anyi Rao,
Nanxuan Zhao,
Bo Dai,
Ziwei Liu,
Dahua Lin
ICCV, 2021
project page
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paper
Use graph-based VAE to automatically learn from large
amount of vectorized public urban planning data for fast
generation of batches of diverse and valid city block
templates.
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Real or Not Real, that is the Question
Yuanbo Xiangli*, Yubin Deng*,
Bo Dai*,
Chen Change Loy,
Dahua Lin
ICLR, 2020 Spotlight
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video
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zhihu
The proposed realness distribution provides stronger
guidance to the generator and encourages it to learn more
diverse outputs; enables the simplest GAN structure to
synthesis high resolution portrait for the first time,
with affordable computational overhead.
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Autonomous Learning of Speaker Identity and WiFi Geofence
from Noisy Sensor Data
Chris Xiaoxuan Lu, Yuanbo Xiangli,
Peijun Zhao,
Changhao Chen,
Niki Trigoni,
Andrew Markham
IEEE Internet Things J., 2019
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iSCAN: automatic speaker adaptation via iterative
cross-modality association
Yuanbo Xiangli,
Chris Xiaoxuan Lu,
Peijun Zhao,
Changhao Chen,
Andrew Markham
UbiComp/ISWC Adjunct, 2019
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The proposed framework leverages the abundant side-channel
information provided by the ubiquitous IoT environment in
mordern life, enabling the construction of an in-domain
speaker recognition model with zero human enrollment.
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The website template was borrowed from
Jon Baron. Thanks for the generosity :)
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