6 citations · 16 across the 14 of their papers we have counts for
19 papers
Real2Gym: Building Gyms from Videos, Bringing Skills to Robots
Kerui Ren, Yingxiang Xu, Kaiwen Song +4
Real-world videos provide rich demonstrations of manipulation, but turning them into reusable robot skills requires visually aligned environments, executable physical interactions,…
M^3: Dense Matching Meets Multi-View Foundation Models for Monocular Gaussian Splatting SLAM
Kerui Ren, Guanghao Li, Changjian Jiang +7
Streaming reconstruction from uncalibrated monocular video remains challenging, as it requires both high-precision pose estimation and computationally efficient online refinement i…
SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation
Mu Huang, Hui Wang, Kerui Ren +5
Simulating deformable objects under rich interactions remains a fundamental challenge for real-to-sim robot manipulation, with dynamics jointly driven by environmental effects and…
EAG-PT: Emission-Aware Gaussians and Path Tracing for Diffuse Indoor Scene Reconstruction and Editing
Xijie Yang, Mulin Yu, Changjian Jiang +6
Recent radiance-field-based reconstruction methods, such as NeRF and 3DGS, achieve high visual fidelity for indoor scenes, but often break down under scene editing due to baked ill…
PLANING: A Loosely Coupled Triangle-Gaussian Framework for Streaming 3D Reconstruction
Changjian Jiang, Kerui Ren, Xudong Li +8
Streaming reconstruction from monocular image sequences remains challenging, as existing methods typically favor either high-quality rendering or accurate geometry, but rarely both…
ARTDECO: Towards Efficient and High-Fidelity On-the-Fly 3D Reconstruction with Structured Scene Representation
Guanghao Li, Kerui Ren, Linning Xu +7
On-the-fly 3D reconstruction from monocular image sequences is a long-standing challenge in computer vision, critical for applications such as real-to-sim, AR/VR, and robotics. Exi…
Reversible GNS for Dissipative Fluids with Consistent Bidirectional Dynamics
Mu Huang, Linning Xu, Mingyue Dai +2
Simulating physically plausible trajectories toward user-defined goals is a fundamental yet challenging task in fluid dynamics. While particle-based simulators can efficiently repr…
ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via Gaussian Splatting
Ruijie Zhu, Mulin Yu, Linning Xu +5
3D Gaussian Splatting is renowned for its high-fidelity reconstructions and real-time novel view synthesis, yet its lack of semantic understanding limits object-level perception. I…
Direct Numerical Layout Generation for 3D Indoor Scene Synthesis via Spatial Reasoning
Xingjian Ran, Yixuan Li, Linning Xu +2
Realistic 3D indoor scene synthesis is vital for embodied AI and digital content creation. It can be naturally divided into two subtasks: object generation and layout generation. W…
MV-CoLight: Efficient Object Compositing with Consistent Lighting and Shadow Generation
Kerui Ren, Jiayang Bai, Linning Xu +4
Object compositing offers significant promise for augmented reality (AR) and embodied intelligence applications. Existing approaches predominantly focus on single-image scenarios o…
HaloGS: Loose Coupling of Compact Geometry and Gaussian Splats for 3D Scenes
Changjian Jiang, Kerui Ren, Linning Xu +5
High fidelity 3D reconstruction and rendering hinge on capturing precise geometry while preserving photo realistic detail. Most existing methods either fuse these goals into a sing…
Virtualized 3D Gaussians: Flexible Cluster-based Level-of-Detail System for Real-Time Rendering of Composed Scenes
Xijie Yang, Linning Xu, Lihan Jiang +2
3D Gaussian Splatting (3DGS) enables the reconstruction of intricate digital 3D assets from multi-view images by leveraging a set of 3D Gaussian primitives for rendering. Its expli…
AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views
Lihan Jiang, Yucheng Mao, Linning Xu +9
We introduce AnySplat, a feed forward network for novel view synthesis from uncalibrated image collections. In contrast to traditional neural rendering pipelines that demand known…
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…
Horizon-GS: Unified 3D Gaussian Splatting for Large-Scale Aerial-to-Ground Scenes
Lihan Jiang, Kerui Ren, Mulin Yu +6
Seamless integration of both aerial and street view images remains a significant challenge in neural scene reconstruction and rendering. Existing methods predominantly focus on sin…
Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text
Xinyang Li, Zhangyu Lai, Linning Xu +5
Recent advancements in 3D generation have leveraged synthetic datasets with ground truth 3D assets and predefined cameras. However, the potential of adopting real-world datasets, w…
Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode Multi-view Latent Diffusion
Xinyang Li, Zhangyu Lai, Linning Xu +5
We present Dual3D, a novel text-to-3D generation framework that generates high-quality 3D assets from texts in only minute.The key component is a dual-mode multi-view latent di…
Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians
Kerui Ren, Lihan Jiang, Tao Lu +4
The recent 3D Gaussian splatting (3D-GS) has shown remarkable rendering fidelity and efficiency compared to NeRF-based neural scene representations. While demonstrating the potenti…
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…