18 citations · 21 across the 7 of their papers we have counts for
7 papers
Pano2World: End-to-End 3D Generation via Unified Multi-View Sequences
Zhenjia Li, Jinrang Jia, Yifeng Shi
A single panorama captures the full visual sphere from one camera center, yet confines users to looking around in place without enabling true scene exploration. Converting a single…
PanoWorld: A Generative Spatial World Model for Consistent Whole-House Panorama Synthesis
Jinrang Jia, Zhenjia Li, Yijiang Hu +1
Generating a consistent whole-house VR tour from a floorplan and style reference requires both photorealistic panoramas and cross-view spatial coherence. Pure 2D generators produce…
You Only Gaussian Once: Controllable 3D Gaussian Splatting for Ultra-Densely Sampled Scenes
Jinrang Jia, Zhenjia Li, Yifeng Shi
3D Gaussian Splatting (3DGS) has revolutionized neural rendering, yet existing methods remain predominantly research prototypes ill-suited for production-level deployment. We ident…
CRUISE: Cooperative Reconstruction and Editing in V2X Scenarios using Gaussian Splatting
Haoran Xu, Saining Zhang, Peishuo Li +15
Vehicle-to-everything (V2X) communication plays a crucial role in autonomous driving, enabling cooperation between vehicles and infrastructure. While simulation has significantly c…
RopeBEV: A Multi-Camera Roadside Perception Network in Bird's-Eye-View
Jinrang Jia, Guangqi Yi, Yifeng Shi
Multi-camera perception methods in Bird's-Eye-View (BEV) have gained wide application in autonomous driving. However, due to the differences between roadside and vehicle-side scena…
MonoLSS: Learnable Sample Selection For Monocular 3D Detection
Zhenjia Li, Jinrang Jia, Yifeng Shi
In the field of autonomous driving, monocular 3D detection is a critical task which estimates 3D properties (depth, dimension, and orientation) of objects in a single RGB image. Pr…
DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative Perception
Xianghao Kong, Wentao Jiang, Jinrang Jia +3
Vehicle-to-Everything (V2X) collaborative perception is crucial for autonomous driving. However, achieving high-precision V2X perception requires a significant amount of annotated…